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1. add verbose switch. 

2. fix two dimension bugs in model selection function when computing std.
v0.1
jajupmochi 6 years ago
parent
04bc3784cf
commit
7b75c1fc97
19 changed files with 714 additions and 694 deletions
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  1. +2
    -1
      notebooks/run_commonwalkkernel.ipynb
  2. +2
    -1
      notebooks/run_commonwalkkernel.py
  3. +2
    -1
      notebooks/run_marginalizedkernel.ipynb
  4. +2
    -1
      notebooks/run_marginalizedkernel.py
  5. +79
    -72
      notebooks/run_randomwalkkernel.ipynb
  6. +2
    -1
      notebooks/run_randomwalkkernel.py
  7. +52
    -78
      notebooks/run_spkernel.ipynb
  8. +2
    -1
      notebooks/run_spkernel.py
  9. +36
    -104
      notebooks/run_structuralspkernel.ipynb
  10. +2
    -1
      notebooks/run_structuralspkernel.py
  11. +117
    -259
      notebooks/run_untilhpathkernel.ipynb
  12. +2
    -1
      notebooks/run_untilhpathkernel.py
  13. +180
    -7
      preimage/pathfrequency.py
  14. +9
    -7
      pygraph/kernels/commonWalkKernel.py
  15. +49
    -39
      pygraph/kernels/randomWalkKernel.py
  16. +23
    -15
      pygraph/kernels/spKernel.py
  17. +24
    -18
      pygraph/kernels/structuralspKernel.py
  18. +13
    -9
      pygraph/kernels/untilHPathKernel.py
  19. +116
    -78
      pygraph/utils/model_selection_precomputed.py

+ 2
- 1
notebooks/run_commonwalkkernel.ipynb View File

@@ -138,7 +138,8 @@
" extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
" ds_name=ds['name'],\n",
" n_jobs=multiprocessing.cpu_count(),\n",
" read_gm_from_file=False)\n",
" read_gm_from_file=False,\n",
" verbose=True)\n",
" print()"
]
}


+ 2
- 1
notebooks/run_commonwalkkernel.py View File

@@ -82,5 +82,6 @@ for ds in dslist:
extra_params=(ds['extra_params'] if 'extra_params' in ds else None),
ds_name=ds['name'],
n_jobs=multiprocessing.cpu_count(),
read_gm_from_file=False)
read_gm_from_file=False,
verbose=True)
print()

+ 2
- 1
notebooks/run_marginalizedkernel.ipynb View File

@@ -170,7 +170,8 @@
" extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
" ds_name=ds['name'],\n",
" n_jobs=multiprocessing.cpu_count(),\n",
" read_gm_from_file=False)\n",
" read_gm_from_file=False,\n",
" verbose=True)\n",
" print()"
]
}


+ 2
- 1
notebooks/run_marginalizedkernel.py View File

@@ -81,5 +81,6 @@ for ds in dslist:
extra_params=(ds['extra_params'] if 'extra_params' in ds else None),
ds_name=ds['name'],
n_jobs=multiprocessing.cpu_count(),
read_gm_from_file=False)
read_gm_from_file=False,
verbose=True)
print()

+ 79
- 72
notebooks/run_randomwalkkernel.ipynb View File

@@ -23,14 +23,14 @@
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5067.96it/s]\n"
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5308.25it/s]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"../pygraph/kernels/randomWalkKernel.py:104: UserWarning: All labels are ignored.\n",
"../pygraph/kernels/randomWalkKernel.py:108: UserWarning: All labels are ignored.\n",
" warnings.warn('All labels are ignored.')\n"
]
},
@@ -38,132 +38,132 @@
"name": "stdout",
"output_type": "stream",
"text": [
"calculating kernels: 16836it [00:00, 76328.32it/s]\n",
"calculating kernels: 16836it [00:00, 65408.89it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.38956499099731445 seconds ---\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.4157981872558594 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.1, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.1, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5928.69it/s]\n",
"calculating kernels: 16836it [00:00, 76159.65it/s]\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5205.09it/s]\n",
"calculating kernels: 16836it [00:00, 73715.56it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.3680381774902344 seconds ---\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.36714887619018555 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.01, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.01, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5599.99it/s]\n",
"calculating kernels: 16836it [00:00, 68122.63it/s]\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5344.96it/s]\n",
"calculating kernels: 16836it [00:00, 68817.65it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.37236690521240234 seconds ---\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.3666379451751709 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.001, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.001, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5444.75it/s]\n",
"calculating kernels: 16836it [00:00, 65554.92it/s]\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5295.73it/s]\n",
"calculating kernels: 16836it [00:00, 74865.49it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.37699007987976074 seconds ---\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.36979222297668457 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.0001, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.0001, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4451.65it/s]\n",
"calculating kernels: 16836it [00:00, 64227.38it/s]\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5040.80it/s]\n",
"calculating kernels: 16836it [00:00, 70923.54it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.40868353843688965 seconds ---\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.3692610263824463 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-05, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-05, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5455.55it/s]\n",
"calculating kernels: 16836it [00:00, 69253.03it/s]\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5326.60it/s]\n",
"calculating kernels: 16836it [00:00, 73697.55it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.4017300605773926 seconds ---\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.37317800521850586 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-06, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-06, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5691.60it/s]\n",
"calculating kernels: 16836it [00:00, 67461.93it/s]\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5705.98it/s]\n",
"calculating kernels: 16836it [00:00, 64238.65it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.3712270259857178 seconds ---\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.36565732955932617 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-07, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-07, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5229.98it/s]\n",
"calculating kernels: 16836it [00:00, 70363.33it/s]\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4833.15it/s]\n",
"calculating kernels: 16836it [00:00, 69971.77it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.37551283836364746 seconds ---\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.37798523902893066 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-08, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-08, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5023.84it/s]\n",
"calculating kernels: 16836it [00:00, 71457.21it/s]\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4170.94it/s]\n",
"calculating kernels: 16836it [00:00, 64187.38it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.39424848556518555 seconds ---\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.39433860778808594 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-09, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-09, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5359.14it/s]\n",
"calculating kernels: 16836it [00:00, 73095.73it/s]\n",
"compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5273.43it/s]\n",
"calculating kernels: 16836it [00:00, 69555.28it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.37821507453918457 seconds ---\n",
" --- kernel matrix of random walk kernel of size 183 built in 0.3833920955657959 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-10, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-10, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
"10 gram matrices are calculated, 0 of which are ignored.\n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 30it [00:33, 1.10s/it]\n",
"cross validation: 30it [00:33, 1.11s/it]\n",
"\n",
"4. Getting final performance...\n",
"best_params_out: [{'compute_method': 'sylvester', 'weight': 0.01, 'n_jobs': 8}]\n",
"best_params_in: [{'alpha': 3.1622776601683795e-10}]\n",
"best_params_out: [{'compute_method': 'sylvester', 'weight': 0.01, 'n_jobs': 8, 'verbose': True}]\n",
"best_params_in: [{'alpha': 1e-10}]\n",
"\n",
"best_val_perf: 31.894498817348637\n",
"best_val_std: 0.5235865101548381\n",
"final_performance: [32.04579601256704]\n",
"final_confidence: [3.9737533137212138]\n",
"train_performance: [30.830572265896325]\n",
"train_std: [0.44072573889937117]\n",
"best_val_perf: 31.76835551233969\n",
"best_val_std: 0.43269972907929183\n",
"final_performance: [32.391882524496765]\n",
"final_confidence: [2.6542337929023336]\n",
"train_performance: [30.70127313658435]\n",
"train_std: [0.31861204198126475]\n",
"\n",
"time to calculate gram matrix with different hyper-params: 0.38±0.01s\n",
"time to calculate best gram matrix: 0.37±nans\n",
"total training time with all hyper-param choices: 40.27s\n",
"time to calculate gram matrix with different hyper-params: 0.38±0.02s\n",
"time to calculate best gram matrix: 0.37±0.00s\n",
"total training time with all hyper-param choices: 40.53s\n",
"\n",
"\n",
"--- This is a regression problem ---\n",
@@ -175,24 +175,30 @@
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"reindex vertices: 100%|██████████| 183/183 [00:00<00:00, 26008.32it/s]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
" keepdims=keepdims)\n",
"/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
" ret = ret.dtype.type(ret / rcount)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"calculating kernels: 12501it [00:01, 6173.52it/s]"
"reindex vertices: 100%|██████████| 183/183 [00:00<00:00, 28950.24it/s]\n",
"calculating kernels: 16836it [00:02, 6540.43it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 2.6675093173980713 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'conjugate', 'edge_kernels': {'symb': <function deltakernel at 0x7fe9b0a2d730>, 'nsymb': <function gaussiankernel at 0x7fe9b0a2d7b8>, 'mix': functools.partial(<function kernelproduct at 0x7fe9b0a2d8c8>, <function deltakernel at 0x7fe9b0a2d730>, <function gaussiankernel at 0x7fe9b0a2d7b8>)}, 'node_kernels': {'symb': <function deltakernel at 0x7fe9b0a2d730>, 'nsymb': <function gaussiankernel at 0x7fe9b0a2d7b8>, 'mix': functools.partial(<function kernelproduct at 0x7fe9b0a2d8c8>, <function deltakernel at 0x7fe9b0a2d730>, <function gaussiankernel at 0x7fe9b0a2d7b8>)}, 'weight': 0.1, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"reindex vertices: 100%|██████████| 183/183 [00:00<00:00, 28019.19it/s]\n",
"calculating kernels: 16836it [00:02, 7963.48it/s]\n",
"\n",
" --- kernel matrix of random walk kernel of size 183 built in 2.2675061225891113 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'conjugate', 'edge_kernels': {'symb': <function deltakernel at 0x7fe9b0a2d730>, 'nsymb': <function gaussiankernel at 0x7fe9b0a2d7b8>, 'mix': functools.partial(<function kernelproduct at 0x7fe9b0a2d8c8>, <function deltakernel at 0x7fe9b0a2d730>, <function gaussiankernel at 0x7fe9b0a2d7b8>)}, 'node_kernels': {'symb': <function deltakernel at 0x7fe9b0a2d730>, 'nsymb': <function gaussiankernel at 0x7fe9b0a2d7b8>, 'mix': functools.partial(<function kernelproduct at 0x7fe9b0a2d8c8>, <function deltakernel at 0x7fe9b0a2d730>, <function gaussiankernel at 0x7fe9b0a2d7b8>)}, 'weight': 0.01, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"reindex vertices: 100%|██████████| 183/183 [00:00<00:00, 23036.63it/s]\n",
"calculating kernels: 12801it [00:01, 8043.11it/s]"
]
}
],
@@ -299,7 +305,8 @@
" extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
" ds_name=ds['name'],\n",
" n_jobs=multiprocessing.cpu_count(),\n",
" read_gm_from_file=False)\n",
" read_gm_from_file=False,\n",
" verbose=True)\n",
" print()"
]
}


+ 2
- 1
notebooks/run_randomwalkkernel.py View File

@@ -106,5 +106,6 @@ for ds in dslist:
extra_params=(ds['extra_params'] if 'extra_params' in ds else None),
ds_name=ds['name'],
n_jobs=multiprocessing.cpu_count(),
read_gm_from_file=False)
read_gm_from_file=False,
verbose=True)
print()

+ 52
- 78
notebooks/run_spkernel.ipynb View File

@@ -23,34 +23,34 @@
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"getting sp graphs: 183it [00:00, 5345.48it/s]\n",
"calculating kernels: 16836it [00:01, 16066.90it/s]\n",
"getting sp graphs: 183it [00:00, 11704.68it/s]\n",
"calculating kernels: 16836it [00:00, 17085.14it/s]\n",
"\n",
" --- shortest path kernel matrix of size 183 built in 1.2855160236358643 seconds ---\n",
" --- shortest path kernel matrix of size 183 built in 1.2640743255615234 seconds ---\n",
"\n",
"the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7fe84734e598>, 'nsymb': <function gaussiankernel at 0x7fe84734e620>, 'mix': functools.partial(<function kernelproduct at 0x7fe84734e730>, <function deltakernel at 0x7fe84734e598>, <function gaussiankernel at 0x7fe84734e620>)}, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
"1 gram matrices are calculated, 0 of which are ignored.\n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 30it [00:03, 8.63it/s]\n",
"cross validation: 30it [00:03, 8.84it/s]\n",
"\n",
"4. Getting final performance...\n",
"best_params_out: [{'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8}]\n",
"best_params_in: [{'alpha': 0.0001}]\n",
"best_params_out: [{'node_kernels': {'symb': <function deltakernel at 0x7fe84734e598>, 'nsymb': <function gaussiankernel at 0x7fe84734e620>, 'mix': functools.partial(<function kernelproduct at 0x7fe84734e730>, <function deltakernel at 0x7fe84734e598>, <function gaussiankernel at 0x7fe84734e620>)}, 'n_jobs': 8, 'verbose': True}]\n",
"best_params_in: [{'alpha': 1e-10}]\n",
"\n",
"best_val_perf: 9.674788994813262\n",
"best_val_std: 0.6229031522274688\n",
"final_performance: [9.590999824754439]\n",
"final_confidence: [2.911796096257332]\n",
"train_performance: [6.16594412531739]\n",
"train_std: [0.2739093211154806]\n",
"best_val_perf: 9.687399048018559\n",
"best_val_std: 0.654180946161292\n",
"final_performance: [9.411656660355659]\n",
"final_confidence: [2.500437167823725]\n",
"train_performance: [6.168480355249007]\n",
"train_std: [0.2541557651056269]\n",
"\n",
"time to calculate gram matrix with different hyper-params: 1.29±nans\n",
"time to calculate best gram matrix: 1.29±nans\n",
"total training time with all hyper-param choices: 5.15s\n",
"time to calculate gram matrix with different hyper-params: 1.26±0.00s\n",
"time to calculate best gram matrix: 1.26±0.00s\n",
"total training time with all hyper-param choices: 5.09s\n",
"\n",
"\n",
"\n",
@@ -67,51 +67,35 @@
"\n",
"\n",
" 1 graphs are removed as they don't contain edges.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
" keepdims=keepdims)\n",
"/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
" ret = ret.dtype.type(ret / rcount)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"getting sp graphs: 149it [00:00, 6510.18it/s]\n",
"calculating kernels: 11175it [00:00, 18881.68it/s]\n",
"\n",
" --- shortest path kernel matrix of size 149 built in 0.8007419109344482 seconds ---\n",
"getting sp graphs: 149it [00:00, 7096.72it/s]\n",
"calculating kernels: 11175it [00:00, 19504.73it/s]\n",
"\n",
"the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8} is: \n",
" --- shortest path kernel matrix of size 149 built in 0.7957959175109863 seconds ---\n",
"\n",
"the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7fe84734e598>, 'nsymb': <function gaussiankernel at 0x7fe84734e620>, 'mix': functools.partial(<function kernelproduct at 0x7fe84734e730>, <function deltakernel at 0x7fe84734e598>, <function gaussiankernel at 0x7fe84734e620>)}, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
"1 gram matrices are calculated, 0 of which are ignored.\n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 30it [00:02, 10.52it/s]\n",
"cross validation: 30it [00:02, 10.74it/s]\n",
"\n",
"4. Getting final performance...\n",
"best_params_out: [{'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8}]\n",
"best_params_in: [{'alpha': 3.162277660168379e-07}]\n",
"best_params_out: [{'node_kernels': {'symb': <function deltakernel at 0x7fe84734e598>, 'nsymb': <function gaussiankernel at 0x7fe84734e620>, 'mix': functools.partial(<function kernelproduct at 0x7fe84734e730>, <function deltakernel at 0x7fe84734e598>, <function gaussiankernel at 0x7fe84734e620>)}, 'n_jobs': 8, 'verbose': True}]\n",
"best_params_in: [{'alpha': 1e-05}]\n",
"\n",
"best_val_perf: 8.784264102873752\n",
"best_val_std: 0.2656887278835053\n",
"final_performance: [8.059911355753659]\n",
"final_confidence: [1.9620843656589473]\n",
"train_performance: [7.8406202266920575]\n",
"train_std: [0.2177862360087283]\n",
"best_val_perf: 8.745832519261795\n",
"best_val_std: 0.26293501071192543\n",
"final_performance: [7.942686332248635]\n",
"final_confidence: [1.617779657027359]\n",
"train_performance: [7.860965083396337]\n",
"train_std: [0.16888913664254188]\n",
"\n",
"time to calculate gram matrix with different hyper-params: 0.80±nans\n",
"time to calculate best gram matrix: 0.80±nans\n",
"total training time with all hyper-param choices: 4.02s\n",
"time to calculate gram matrix with different hyper-params: 0.80±0.00s\n",
"time to calculate best gram matrix: 0.80±0.00s\n",
"total training time with all hyper-param choices: 3.90s\n",
"\n",
"\n",
"\n",
@@ -126,34 +110,34 @@
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"getting sp graphs: 68it [00:00, 1095.77it/s]\n",
"calculating kernels: 2346it [00:02, 813.63it/s]\n",
"getting sp graphs: 68it [00:00, 2292.58it/s]\n",
"calculating kernels: 2346it [00:02, 873.39it/s]\n",
"\n",
" --- shortest path kernel matrix of size 68 built in 3.110588550567627 seconds ---\n",
" --- shortest path kernel matrix of size 68 built in 2.986046075820923 seconds ---\n",
"\n",
"the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7fe84734e598>, 'nsymb': <function gaussiankernel at 0x7fe84734e620>, 'mix': functools.partial(<function kernelproduct at 0x7fe84734e730>, <function deltakernel at 0x7fe84734e598>, <function gaussiankernel at 0x7fe84734e620>)}, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
"1 gram matrices are calculated, 0 of which are ignored.\n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 30it [00:02, 10.97it/s]\n",
"cross validation: 30it [00:02, 11.85it/s]\n",
"\n",
"4. Getting final performance...\n",
"best_params_out: [{'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8}]\n",
"best_params_out: [{'node_kernels': {'symb': <function deltakernel at 0x7fe84734e598>, 'nsymb': <function gaussiankernel at 0x7fe84734e620>, 'mix': functools.partial(<function kernelproduct at 0x7fe84734e730>, <function deltakernel at 0x7fe84734e598>, <function gaussiankernel at 0x7fe84734e620>)}, 'n_jobs': 8, 'verbose': True}]\n",
"best_params_in: [{'C': 3162.2776601683795}]\n",
"\n",
"best_val_perf: 0.8798412698412699\n",
"best_val_std: 0.02062186442241262\n",
"final_performance: [0.9042857142857144]\n",
"final_confidence: [0.07343487734322982]\n",
"train_performance: [0.9709180695847363]\n",
"train_std: [0.005927396388634032]\n",
"best_val_perf: 0.8780158730158729\n",
"best_val_std: 0.028162670831398482\n",
"final_performance: [0.8714285714285714]\n",
"final_confidence: [0.09446318571439967]\n",
"train_performance: [0.9740729517396185]\n",
"train_std: [0.007872630412568218]\n",
"\n",
"time to calculate gram matrix with different hyper-params: 3.11±nans\n",
"time to calculate best gram matrix: 3.11±nans\n",
"total training time with all hyper-param choices: 6.21s\n",
"time to calculate gram matrix with different hyper-params: 2.99±0.00s\n",
"time to calculate best gram matrix: 2.99±0.00s\n",
"total training time with all hyper-param choices: 5.93s\n",
"\n",
"\n",
"\n",
@@ -168,19 +152,8 @@
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"getting sp graphs: 94it [00:00, 2190.46it/s]\n",
"calculating kernels: 4465it [00:05, 763.81it/s]\n",
"\n",
" --- shortest path kernel matrix of size 94 built in 6.083932399749756 seconds ---\n",
"\n",
"the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8} is: \n",
"\n",
"\n",
"\n",
"1 gram matrices are calculated, 0 of which are ignored.\n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 0it [00:00, ?it/s]"
"getting sp graphs: 94it [00:00, 2131.93it/s]\n",
"calculating kernels: 1501it [00:01, 78.00it/s]"
]
}
],
@@ -264,7 +237,8 @@
" extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
" ds_name=ds['name'],\n",
" n_jobs=multiprocessing.cpu_count(),\n",
" read_gm_from_file=False)\n",
" read_gm_from_file=False,\n",
" verbose=True)\n",
" print()"
]
}


+ 2
- 1
notebooks/run_spkernel.py View File

@@ -77,5 +77,6 @@ for ds in dslist:
extra_params=(ds['extra_params'] if 'extra_params' in ds else None),
ds_name=ds['name'],
n_jobs=multiprocessing.cpu_count(),
read_gm_from_file=False)
read_gm_from_file=False,
verbose=True)
print()

+ 36
- 104
notebooks/run_structuralspkernel.ipynb View File

@@ -23,34 +23,34 @@
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"getting shortest paths: 183it [00:00, 5316.42it/s]\n",
"calculating kernels: 16836it [00:03, 4625.84it/s]\n",
"getting shortest paths: 183it [00:00, 5323.35it/s]\n",
"calculating kernels: 16836it [00:02, 5980.75it/s]\n",
"\n",
" --- shortest path kernel matrix of size 183 built in 3.8611345291137695 seconds ---\n",
" --- shortest path kernel matrix of size 183 built in 3.0884954929351807 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7ff5ffc0c268>, 'nsymb': <function gaussiankernel at 0x7ff5ffc0c2f0>, 'mix': functools.partial(<function kernelproduct at 0x7ff5ffc0c400>, <function deltakernel at 0x7ff5ffc0c268>, <function gaussiankernel at 0x7ff5ffc0c2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7ff5ffc0c268>, 'nsymb': <function gaussiankernel at 0x7ff5ffc0c2f0>, 'mix': functools.partial(<function kernelproduct at 0x7ff5ffc0c400>, <function deltakernel at 0x7ff5ffc0c268>, <function gaussiankernel at 0x7ff5ffc0c2f0>)}, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
"1 gram matrices are calculated, 0 of which are ignored.\n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 30it [00:03, 8.71it/s]\n",
"cross validation: 30it [00:03, 8.90it/s]\n",
"\n",
"4. Getting final performance...\n",
"best_params_out: [{'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'n_jobs': 8}]\n",
"best_params_in: [{'alpha': 0.0031622776601683794}]\n",
"best_params_out: [{'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7ff5ffc0c268>, 'nsymb': <function gaussiankernel at 0x7ff5ffc0c2f0>, 'mix': functools.partial(<function kernelproduct at 0x7ff5ffc0c400>, <function deltakernel at 0x7ff5ffc0c268>, <function gaussiankernel at 0x7ff5ffc0c2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7ff5ffc0c268>, 'nsymb': <function gaussiankernel at 0x7ff5ffc0c2f0>, 'mix': functools.partial(<function kernelproduct at 0x7ff5ffc0c400>, <function deltakernel at 0x7ff5ffc0c268>, <function gaussiankernel at 0x7ff5ffc0c2f0>)}, 'n_jobs': 8, 'verbose': True}]\n",
"best_params_in: [{'alpha': 0.001}]\n",
"\n",
"best_val_perf: 12.673707811197355\n",
"best_val_std: 0.8773195213759171\n",
"final_performance: [12.972668262063593]\n",
"final_confidence: [3.7642237202379087]\n",
"train_performance: [3.934708519599526]\n",
"train_std: [0.16225809646161615]\n",
"best_val_perf: 12.857015647214508\n",
"best_val_std: 0.8860388066269581\n",
"final_performance: [12.157314781928168]\n",
"final_confidence: [2.5739406086892296]\n",
"train_performance: [3.773093745028789]\n",
"train_std: [0.12430822644728814]\n",
"\n",
"time to calculate gram matrix with different hyper-params: 3.86±nans\n",
"time to calculate best gram matrix: 3.86±nans\n",
"total training time with all hyper-param choices: 7.74s\n",
"time to calculate gram matrix with different hyper-params: 3.09±0.00s\n",
"time to calculate best gram matrix: 3.09±0.00s\n",
"total training time with all hyper-param choices: 6.84s\n",
"\n",
"\n",
"\n",
@@ -64,51 +64,35 @@
"2. Calculating gram matrices. This could take a while...\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
" keepdims=keepdims)\n",
"/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
" ret = ret.dtype.type(ret / rcount)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"getting shortest paths: 150it [00:00, 8822.07it/s]\n",
"calculating kernels: 11325it [00:02, 5167.04it/s]\n",
"\n",
" --- shortest path kernel matrix of size 150 built in 2.394453525543213 seconds ---\n",
"getting shortest paths: 150it [00:00, 5191.83it/s]\n",
"calculating kernels: 11325it [00:01, 7143.18it/s]\n",
"\n",
"the gram matrix with parameters {'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'n_jobs': 8} is: \n",
" --- shortest path kernel matrix of size 150 built in 1.7898523807525635 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7ff5ffc0c268>, 'nsymb': <function gaussiankernel at 0x7ff5ffc0c2f0>, 'mix': functools.partial(<function kernelproduct at 0x7ff5ffc0c400>, <function deltakernel at 0x7ff5ffc0c268>, <function gaussiankernel at 0x7ff5ffc0c2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7ff5ffc0c268>, 'nsymb': <function gaussiankernel at 0x7ff5ffc0c2f0>, 'mix': functools.partial(<function kernelproduct at 0x7ff5ffc0c400>, <function deltakernel at 0x7ff5ffc0c268>, <function gaussiankernel at 0x7ff5ffc0c2f0>)}, 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
"1 gram matrices are calculated, 0 of which are ignored.\n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 30it [00:02, 10.78it/s]\n",
"cross validation: 30it [00:02, 10.59it/s]\n",
"\n",
"4. Getting final performance...\n",
"best_params_out: [{'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'n_jobs': 8}]\n",
"best_params_out: [{'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7ff5ffc0c268>, 'nsymb': <function gaussiankernel at 0x7ff5ffc0c2f0>, 'mix': functools.partial(<function kernelproduct at 0x7ff5ffc0c400>, <function deltakernel at 0x7ff5ffc0c268>, <function gaussiankernel at 0x7ff5ffc0c2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7ff5ffc0c268>, 'nsymb': <function gaussiankernel at 0x7ff5ffc0c2f0>, 'mix': functools.partial(<function kernelproduct at 0x7ff5ffc0c400>, <function deltakernel at 0x7ff5ffc0c268>, <function gaussiankernel at 0x7ff5ffc0c2f0>)}, 'n_jobs': 8, 'verbose': True}]\n",
"best_params_in: [{'alpha': 0.1}]\n",
"\n",
"best_val_perf: 11.082918177885857\n",
"best_val_std: 0.3037589925734673\n",
"final_performance: [7.8261546009779925]\n",
"final_confidence: [1.59375970943081]\n",
"train_performance: [7.988630946761633]\n",
"train_std: [0.16054607648943253]\n",
"best_val_perf: 11.040598123045763\n",
"best_val_std: 0.31492017111536147\n",
"final_performance: [8.138193149138093]\n",
"final_confidence: [1.6238744767195439]\n",
"train_performance: [7.9412913127748235]\n",
"train_std: [0.18726339675217385]\n",
"\n",
"time to calculate gram matrix with different hyper-params: 2.39±nans\n",
"time to calculate best gram matrix: 2.39±nans\n",
"total training time with all hyper-param choices: 5.49s\n",
"time to calculate gram matrix with different hyper-params: 1.79±0.00s\n",
"time to calculate best gram matrix: 1.79±0.00s\n",
"total training time with all hyper-param choices: 5.00s\n",
"\n",
"\n",
"\n",
@@ -123,61 +107,8 @@
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"getting shortest paths: 68it [00:00, 567.53it/s]\n",
"calculating kernels: 2346it [00:14, 161.71it/s]\n",
"\n",
" --- shortest path kernel matrix of size 68 built in 14.833482265472412 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'n_jobs': 8} is: \n",
"\n",
"\n",
"\n",
"1 gram matrices are calculated, 0 of which are ignored.\n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 30it [00:02, 13.38it/s]\n",
"\n",
"4. Getting final performance...\n",
"best_params_out: [{'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'n_jobs': 8}]\n",
"best_params_in: [{'C': 1000.0}]\n",
"\n",
"best_val_perf: 0.9084126984126983\n",
"best_val_std: 0.027912022159840448\n",
"final_performance: [0.9085714285714286]\n",
"final_confidence: [0.0879511091875412]\n",
"train_performance: [0.9679438832772166]\n",
"train_std: [0.00754192133247499]\n",
"\n",
"time to calculate gram matrix with different hyper-params: 14.83±nans\n",
"time to calculate best gram matrix: 14.83±nans\n",
"total training time with all hyper-param choices: 17.42s\n",
"\n",
"\n",
"\n",
"PAH\n",
"\n",
"--- This is a classification problem ---\n",
"\n",
"\n",
"1. Loading dataset from file...\n",
"\n",
"2. Calculating gram matrices. This could take a while...\n",
"\n",
" None edge weight specified. Set all weight to 1.\n",
"\n",
"getting shortest paths: 94it [00:00, 447.28it/s]\n",
"calculating kernels: 4465it [01:04, 68.94it/s] \n",
"\n",
" --- shortest path kernel matrix of size 94 built in 65.20552921295166 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'naive', 'edge_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'node_kernels': {'symb': <function deltakernel at 0x7f470f0ad268>, 'nsymb': <function gaussiankernel at 0x7f470f0ad2f0>, 'mix': functools.partial(<function kernelproduct at 0x7f470f0ad400>, <function deltakernel at 0x7f470f0ad268>, <function gaussiankernel at 0x7f470f0ad2f0>)}, 'n_jobs': 8} is: \n",
"\n",
"\n",
"\n",
"1 gram matrices are calculated, 0 of which are ignored.\n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 0it [00:00, ?it/s]"
"getting shortest paths: 68it [00:00, 536.19it/s]\n",
"calculating kernels: 0it [00:00, ?it/s]"
]
}
],
@@ -260,7 +191,8 @@
" extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
" ds_name=ds['name'],\n",
" n_jobs=multiprocessing.cpu_count(),\n",
" read_gm_from_file=False)\n",
" read_gm_from_file=False,\n",
" verbose=True)\n",
" print()"
]
}


+ 2
- 1
notebooks/run_structuralspkernel.py View File

@@ -84,5 +84,6 @@ for ds in dslist:
extra_params=(ds['extra_params'] if 'extra_params' in ds else None),
ds_name=ds['name'],
n_jobs=multiprocessing.cpu_count(),
read_gm_from_file=False)
read_gm_from_file=False,
verbose=True)
print()

+ 117
- 259
notebooks/run_untilhpathkernel.ipynb View File

@@ -20,203 +20,187 @@
"1. Loading dataset from file...\n",
"\n",
"2. Calculating gram matrices. This could take a while...\n",
"getting paths: 183it [00:00, 33583.79it/s]\n",
"calculating kernels: 16836it [00:00, 382919.33it/s]\n",
"getting paths: 183it [00:00, 22697.39it/s]\n",
"calculating kernels: 16836it [00:00, 371524.56it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 1 of size 183 built in 0.28138017654418945 seconds ---\n",
" --- kernel matrix of path kernel up to 1 of size 183 built in 0.27962422370910645 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 49932.19it/s]\n",
"calculating kernels: 16836it [00:00, 339040.24it/s]\n",
"getting paths: 183it [00:00, 35988.26it/s]\n",
"calculating kernels: 16836it [00:00, 444708.75it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 1 of size 183 built in 0.2915959358215332 seconds ---\n",
" --- kernel matrix of path kernel up to 1 of size 183 built in 0.284440279006958 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 13100.71it/s]\n",
"calculating kernels: 16836it [00:00, 195915.25it/s]\n",
"getting paths: 183it [00:00, 26474.81it/s]\n",
"calculating kernels: 16836it [00:00, 215084.65it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 2 of size 183 built in 0.39291882514953613 seconds ---\n",
" --- kernel matrix of path kernel up to 2 of size 183 built in 0.2832369804382324 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 15186.23it/s]\n",
"calculating kernels: 16836it [00:00, 216679.82it/s]\n",
"getting paths: 183it [00:00, 18360.43it/s]\n",
"calculating kernels: 16836it [00:00, 254309.18it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 2 of size 183 built in 0.2922053337097168 seconds ---\n",
" --- kernel matrix of path kernel up to 2 of size 183 built in 0.28844165802001953 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 8410.48it/s]\n",
"calculating kernels: 16836it [00:00, 146690.73it/s]\n",
"getting paths: 183it [00:00, 8687.30it/s]\n",
"calculating kernels: 16836it [00:00, 168741.96it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 3 of size 183 built in 0.3915746212005615 seconds ---\n",
" --- kernel matrix of path kernel up to 3 of size 183 built in 0.38907885551452637 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 13951.28it/s]\n",
"calculating kernels: 16836it [00:00, 201673.88it/s]\n",
"getting paths: 183it [00:00, 11379.65it/s]\n",
"calculating kernels: 16836it [00:00, 195770.23it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 3 of size 183 built in 0.3854410648345947 seconds ---\n",
" --- kernel matrix of path kernel up to 3 of size 183 built in 0.39213061332702637 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 10054.46it/s]\n",
"calculating kernels: 16836it [00:00, 70713.10it/s]\n",
"getting paths: 183it [00:00, 8062.50it/s]\n",
"calculating kernels: 16836it [00:00, 72349.59it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 4 of size 183 built in 0.48105573654174805 seconds ---\n",
" --- kernel matrix of path kernel up to 4 of size 183 built in 0.512467622756958 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 1418.94it/s]\n",
"calculating kernels: 16836it [00:00, 144898.57it/s]\n",
"getting paths: 183it [00:00, 10578.68it/s]\n",
"calculating kernels: 16836it [00:00, 133704.13it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 4 of size 183 built in 0.5477819442749023 seconds ---\n",
" --- kernel matrix of path kernel up to 4 of size 183 built in 0.3866546154022217 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 15604.25it/s]\n",
"calculating kernels: 16836it [00:00, 103300.82it/s]\n",
"getting paths: 183it [00:00, 9220.91it/s]\n",
"calculating kernels: 16836it [00:00, 98386.86it/s] \n",
"\n",
" --- kernel matrix of path kernel up to 5 of size 183 built in 0.3788299560546875 seconds ---\n",
" --- kernel matrix of path kernel up to 5 of size 183 built in 0.38112974166870117 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 9795.27it/s]\n",
"calculating kernels: 16836it [00:00, 121689.68it/s]\n",
"getting paths: 183it [00:00, 8493.03it/s]\n",
"calculating kernels: 16836it [00:00, 119698.11it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 5 of size 183 built in 0.3888108730316162 seconds ---\n",
" --- kernel matrix of path kernel up to 5 of size 183 built in 0.38007307052612305 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 7163.19it/s]\n",
"calculating kernels: 16836it [00:00, 89262.17it/s]\n",
"getting paths: 183it [00:00, 7385.55it/s]\n",
"calculating kernels: 16836it [00:00, 88347.09it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 6 of size 183 built in 0.39624905586242676 seconds ---\n",
" --- kernel matrix of path kernel up to 6 of size 183 built in 0.3929023742675781 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 16751.59it/s]\n",
"calculating kernels: 16836it [00:00, 100004.39it/s]\n",
"getting paths: 183it [00:00, 5394.24it/s]\n",
"calculating kernels: 16836it [00:00, 100946.78it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 6 of size 183 built in 0.388913631439209 seconds ---\n",
" --- kernel matrix of path kernel up to 6 of size 183 built in 0.3824801445007324 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 10090.81it/s]\n",
"calculating kernels: 16836it [00:00, 91172.28it/s] \n",
"getting paths: 183it [00:00, 12457.52it/s]\n",
"calculating kernels: 16836it [00:00, 68995.02it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 7 of size 183 built in 0.4908461570739746 seconds ---\n",
" --- kernel matrix of path kernel up to 7 of size 183 built in 0.49313783645629883 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 2997.78it/s]\n",
"calculating kernels: 16836it [00:00, 104945.65it/s]\n",
"getting paths: 183it [00:00, 2829.00it/s]\n",
"calculating kernels: 16836it [00:00, 105515.66it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 7 of size 183 built in 0.36611366271972656 seconds ---\n",
" --- kernel matrix of path kernel up to 7 of size 183 built in 0.35750555992126465 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 6353.90it/s]\n",
"calculating kernels: 16836it [00:00, 80425.25it/s]\n",
"getting paths: 183it [00:00, 7427.43it/s]\n",
"calculating kernels: 16836it [00:00, 81607.79it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 8 of size 183 built in 0.5061323642730713 seconds ---\n",
" --- kernel matrix of path kernel up to 8 of size 183 built in 0.4937615394592285 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 9427.60it/s]\n",
"calculating kernels: 16836it [00:00, 93863.88it/s] \n",
"getting paths: 183it [00:00, 5660.08it/s]\n",
"calculating kernels: 16836it [00:00, 90014.85it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 8 of size 183 built in 0.3872077465057373 seconds ---\n",
" --- kernel matrix of path kernel up to 8 of size 183 built in 0.36504673957824707 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 7575.20it/s]\n",
"calculating kernels: 16836it [00:00, 82517.07it/s]\n",
"getting paths: 183it [00:00, 7548.83it/s]\n",
"calculating kernels: 16836it [00:00, 79498.55it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 9 of size 183 built in 0.48129963874816895 seconds ---\n",
" --- kernel matrix of path kernel up to 9 of size 183 built in 0.47993040084838867 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 6563.74it/s]\n",
"calculating kernels: 16836it [00:00, 94045.02it/s] \n",
"getting paths: 183it [00:00, 7319.90it/s]\n",
"calculating kernels: 16836it [00:00, 92310.24it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 9 of size 183 built in 0.39592933654785156 seconds ---\n",
" --- kernel matrix of path kernel up to 9 of size 183 built in 0.3970515727996826 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 6069.81it/s]\n",
"calculating kernels: 16836it [00:00, 77447.83it/s]\n",
"getting paths: 183it [00:00, 8318.60it/s]\n",
"calculating kernels: 16836it [00:00, 89934.38it/s] \n",
"\n",
" --- kernel matrix of path kernel up to 10 of size 183 built in 0.47420382499694824 seconds ---\n",
" --- kernel matrix of path kernel up to 10 of size 183 built in 0.4861469268798828 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 183it [00:00, 9481.17it/s]\n",
"calculating kernels: 16836it [00:00, 58253.60it/s]\n",
"getting paths: 183it [00:00, 2635.72it/s]\n",
"calculating kernels: 16836it [00:00, 90123.30it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 10 of size 183 built in 0.4869115352630615 seconds ---\n",
" --- kernel matrix of path kernel up to 10 of size 183 built in 0.367603063583374 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"\n",
"20 gram matrices are calculated, 0 of which are ignored.\n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 30it [01:07, 1.10s/it]\n",
"cross validation: 30it [01:06, 1.11s/it]\n",
"\n",
"4. Getting final performance...\n",
"best_params_out: [{'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8}]\n",
"best_params_out: [{'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True}]\n",
"best_params_in: [{'alpha': 0.01}]\n",
"\n",
"best_val_perf: 6.8347760734601675\n",
"best_val_std: 0.26187601855914455\n",
"final_performance: [6.844597847292873]\n",
"final_confidence: [1.3282917788841784]\n",
"train_performance: [2.2886614412566524]\n",
"train_std: [0.11697823620293107]\n",
"best_val_perf: 6.842702754673377\n",
"best_val_std: 0.3600238142615252\n",
"final_performance: [7.557191252340816]\n",
"final_confidence: [2.5849069582911595]\n",
"train_performance: [2.276370048287339]\n",
"train_std: [0.13830866732067562]\n",
"\n",
"time to calculate gram matrix with different hyper-params: 0.39±0.07s\n",
"time to calculate best gram matrix: 0.28±0.00s\n",
"total training time with all hyper-param choices: 79.82s\n",
"\n",
"time to calculate gram matrix with different hyper-params: 0.41±0.07s\n",
"time to calculate best gram matrix: 0.39±nans\n",
"total training time with all hyper-param choices: 82.00s\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
" keepdims=keepdims)\n",
"/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
" ret = ret.dtype.type(ret / rcount)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Alkane\n",
@@ -227,171 +211,44 @@
"1. Loading dataset from file...\n",
"\n",
"2. Calculating gram matrices. This could take a while...\n",
"getting paths: 150it [00:00, 38060.83it/s]\n",
"calculating kernels: 11325it [00:00, 447307.64it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 1 of size 150 built in 0.29852986335754395 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 16860.39it/s]\n",
"calculating kernels: 11325it [00:00, 522115.40it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 1 of size 150 built in 0.29816317558288574 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 18149.30it/s]\n",
"calculating kernels: 11325it [00:00, 383173.55it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 2 of size 150 built in 0.29796385765075684 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 23172.10it/s]\n",
"calculating kernels: 11325it [00:00, 427074.37it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 2 of size 150 built in 0.3109288215637207 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 13243.78it/s]\n",
"calculating kernels: 11325it [00:00, 269283.28it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 3 of size 150 built in 0.29997825622558594 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 20671.78it/s]\n",
"calculating kernels: 11325it [00:00, 312080.29it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 3 of size 150 built in 0.29572534561157227 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 15393.45it/s]\n",
"calculating kernels: 11325it [00:00, 362928.87it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 4 of size 150 built in 0.30132484436035156 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 16957.65it/s]\n",
"calculating kernels: 11325it [00:00, 84503.61it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 4 of size 150 built in 0.4178507328033447 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 14440.54it/s]\n",
"calculating kernels: 11325it [00:00, 276894.92it/s]\n",
"getting paths: 150it [00:00, 31366.32it/s]\n",
"calculating kernels: 11325it [00:00, 509820.58it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 5 of size 150 built in 0.29338693618774414 seconds ---\n",
" --- kernel matrix of path kernel up to 1 of size 150 built in 0.29791831970214844 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 15619.30it/s]\n",
"calculating kernels: 11325it [00:00, 254676.58it/s]\n",
"getting paths: 150it [00:00, 30330.50it/s]\n",
"calculating kernels: 11325it [00:00, 655613.27it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 5 of size 150 built in 0.29663729667663574 seconds ---\n",
" --- kernel matrix of path kernel up to 1 of size 150 built in 0.29232001304626465 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 14585.84it/s]\n",
"calculating kernels: 11325it [00:00, 242964.30it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 6 of size 150 built in 0.29677391052246094 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 11555.83it/s]\n",
"calculating kernels: 11325it [00:00, 330949.31it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 6 of size 150 built in 0.2948622703552246 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 10424.60it/s]\n",
"calculating kernels: 11325it [00:00, 238514.96it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 7 of size 150 built in 0.3041496276855469 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 12318.80it/s]\n",
"calculating kernels: 11325it [00:00, 251979.97it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 7 of size 150 built in 0.3013496398925781 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 10722.00it/s]\n",
"calculating kernels: 11325it [00:00, 232363.74it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 8 of size 150 built in 0.294144868850708 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 18965.59it/s]\n",
"calculating kernels: 11325it [00:00, 424638.55it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 8 of size 150 built in 0.2961091995239258 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 13945.38it/s]\n",
"calculating kernels: 11325it [00:00, 286344.19it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 9 of size 150 built in 0.30029296875 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 9525.87it/s]\n",
"calculating kernels: 11325it [00:00, 231776.43it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 9 of size 150 built in 0.29835057258605957 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"\n",
"\n",
"getting paths: 150it [00:00, 16916.15it/s]\n",
"calculating kernels: 11325it [00:00, 85396.60it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 10 of size 150 built in 0.42621588706970215 seconds ---\n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
"\n",
"getting paths: 150it [00:00, 27568.71it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"calculating kernels: 11325it [00:00, 780628.98it/s]\n",
"\n",
"getting paths: 150it [00:00, 16108.40it/s]\n",
"calculating kernels: 11325it [00:00, 328896.12it/s]\n",
" --- kernel matrix of path kernel up to 2 of size 150 built in 0.2590019702911377 seconds ---\n",
"\n",
" --- kernel matrix of path kernel up to 10 of size 150 built in 0.301084041595459 seconds ---\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
"\n",
"getting paths: 150it [00:00, 17554.29it/s]\n",
"calculating kernels: 11325it [00:00, 320784.55it/s]\n",
"\n",
" --- kernel matrix of path kernel up to 2 of size 150 built in 0.3091611862182617 seconds ---\n",
"\n",
"20 gram matrices are calculated, 0 of which are ignored.\n",
"the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n",
"\n",
"3. Fitting and predicting using nested cross validation. This could really take a while...\n",
"cross validation: 0it [00:00, ?it/s]"
"\n"
]
}
],
@@ -472,7 +329,8 @@
" extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
" ds_name=ds['name'],\n",
" n_jobs=multiprocessing.cpu_count(),\n",
" read_gm_from_file=False)\n",
" read_gm_from_file=False,\n",
" verbose=True)\n",
" print()"
]
},


+ 2
- 1
notebooks/run_untilhpathkernel.py View File

@@ -80,5 +80,6 @@ for ds in dslist:
extra_params=(ds['extra_params'] if 'extra_params' in ds else None),
ds_name=ds['name'],
n_jobs=multiprocessing.cpu_count(),
read_gm_from_file=False)
read_gm_from_file=False,
verbose=True)
print()

+ 180
- 7
preimage/pathfrequency.py View File

@@ -6,23 +6,196 @@ 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(K, v):

def GIPF_tree(v_obj, K=1, alphabet=[0, 1]):
if K == 1:
pass
if G:
return G
else:
return 'no solution'
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
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)

+ 9
- 7
pygraph/kernels/commonWalkKernel.py View File

@@ -26,7 +26,8 @@ def commonwalkkernel(*args,
n=None,
weight=1,
compute_method=None,
n_jobs=None):
n_jobs=None,
verbose=True):
"""Calculate common walk graph kernels between graphs.
Parameters
----------
@@ -71,8 +72,9 @@ def commonwalkkernel(*args,
idx = [G[0] for G in Gn]
Gn = [G[1] for G in Gn]
if len(Gn) != len_gn:
print('\n %d graphs are removed as they have only 1 node.\n' %
(len_gn - len(Gn)))
if verbose:
print('\n %d graphs are removed as they have only 1 node.\n' %
(len_gn - len(Gn)))
ds_attrs = get_dataset_attributes(
Gn,
@@ -102,7 +104,7 @@ def commonwalkkernel(*args,
elif compute_method == 'geo':
do_partial = partial(wrapper_cw_geo, node_label, edge_label, weight)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(Gn,), n_jobs=n_jobs)
glbv=(Gn,), n_jobs=n_jobs, verbose=verbose)
# pool = Pool(n_jobs)
@@ -167,9 +169,9 @@ def commonwalkkernel(*args,
# Kmatrix[j][i] = Kmatrix[i][j]

run_time = time.time() - start_time
print(
"\n --- kernel matrix of common walk kernel of size %d built in %s seconds ---"
% (len(Gn), run_time))
if verbose:
print("\n --- kernel matrix of common walk kernel of size %d built in %s seconds ---"
% (len(Gn), run_time))

return Kmatrix, run_time, idx



+ 49
- 39
pygraph/kernels/randomWalkKernel.py View File

@@ -32,7 +32,8 @@ def randomwalkkernel(*args,
edge_label='bond_type',
# params for spectral method.
sub_kernel=None,
n_jobs=None):
n_jobs=None,
verbose=True):
"""Calculate random walk graph kernels.
Parameters
----------
@@ -60,7 +61,8 @@ def randomwalkkernel(*args,

eweight = None
if edge_weight == None:
print('\n None edge weight specified. Set all weight to 1.\n')
if verbose:
print('\n None edge weight specified. Set all weight to 1.\n')
else:
try:
some_weight = list(
@@ -68,13 +70,13 @@ def randomwalkkernel(*args,
if isinstance(some_weight, float) or isinstance(some_weight, int):
eweight = edge_weight
else:
print(
'\n Edge weight with name %s is not float or integer. Set all weight to 1.\n'
% edge_weight)
if verbose:
print('\n Edge weight with name %s is not float or integer. Set all weight to 1.\n'
% edge_weight)
except:
print(
'\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n'
% edge_weight)
if verbose:
print('\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n'
% edge_weight)

ds_attrs = get_dataset_attributes(
Gn,
@@ -90,8 +92,9 @@ def randomwalkkernel(*args,
idx = [G[0] for G in Gn]
Gn = [G[1] for G in Gn]
if len(Gn) != len_gn:
print('\n %d graphs are removed as they don\'t contain edges.\n' %
(len_gn - len(Gn)))
if verbose:
print('\n %d graphs are removed as they don\'t contain edges.\n' %
(len_gn - len(Gn)))

start_time = time.time()
@@ -100,26 +103,30 @@ def randomwalkkernel(*args,
# gmf = filterGramMatrix(A_wave_list[0], label_list[0], ('C', '0', 'O'), ds_attrs['is_directed'])

if compute_method == 'sylvester':
import warnings
warnings.warn('All labels are ignored.')
Kmatrix = _sylvester_equation(Gn, weight, p, q, eweight, n_jobs)
if verbose:
import warnings
warnings.warn('All labels are ignored.')
Kmatrix = _sylvester_equation(Gn, weight, p, q, eweight, n_jobs, verbose=verbose)

elif compute_method == 'conjugate':
Kmatrix = _conjugate_gradient(Gn, weight, p, q, ds_attrs,
node_kernels, edge_kernels,
node_label, edge_label, eweight, n_jobs)
Kmatrix = _conjugate_gradient(Gn, weight, p, q, ds_attrs, node_kernels,
edge_kernels, node_label, edge_label,
eweight, n_jobs, verbose=verbose)
elif compute_method == 'fp':
Kmatrix = _fixed_point(Gn, weight, p, q, ds_attrs, node_kernels,
edge_kernels, node_label, edge_label,
eweight, n_jobs)
edge_kernels, node_label, edge_label,
eweight, n_jobs, verbose=verbose)

elif compute_method == 'spectral':
import warnings
warnings.warn('All labels are ignored. Only works for undirected graphs.')
Kmatrix = _spectral_decomposition(Gn, weight, p, q, sub_kernel, eweight, n_jobs)
if verbose:
import warnings
warnings.warn('All labels are ignored. Only works for undirected graphs.')
Kmatrix = _spectral_decomposition(Gn, weight, p, q, sub_kernel,
eweight, n_jobs, verbose=verbose)

elif compute_method == 'kron':
pass
for i in range(0, len(Gn)):
for j in range(i, len(Gn)):
Kmatrix[i][j] = _randomwalkkernel_kron(Gn[i], Gn[j],
@@ -131,15 +138,15 @@ def randomwalkkernel(*args,
)

run_time = time.time() - start_time
print(
"\n --- kernel matrix of random walk kernel of size %d built in %s seconds ---"
% (len(Gn), run_time))
if verbose:
print("\n --- kernel matrix of random walk kernel of size %d built in %s seconds ---"
% (len(Gn), run_time))

return Kmatrix, run_time, idx


###############################################################################
def _sylvester_equation(Gn, lmda, p, q, eweight, n_jobs):
def _sylvester_equation(Gn, lmda, p, q, eweight, n_jobs, verbose=True):
"""Calculate walk graph kernels up to n between 2 graphs using Sylvester method.

Parameters
@@ -162,8 +169,9 @@ def _sylvester_equation(Gn, lmda, p, q, eweight, n_jobs):
# don't normalize adjacency matrices if q is a uniform vector. Note
# A_wave_list accually contains the transposes of the adjacency matrices.
A_wave_list = [
nx.adjacency_matrix(G, eweight).todense().transpose() for G in tqdm(
Gn, desc='compute adjacency matrices', file=sys.stdout)
nx.adjacency_matrix(G, eweight).todense().transpose() for G in
(tqdm(Gn, desc='compute adjacency matrices', file=sys.stdout) if
verbose else Gn)
]
# # normalized adjacency matrices
# A_wave_list = []
@@ -178,7 +186,7 @@ def _sylvester_equation(Gn, lmda, p, q, eweight, n_jobs):
G_Awl = Awl_toshare
do_partial = partial(wrapper_se_do, lmda)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(A_wave_list,), n_jobs=n_jobs)
glbv=(A_wave_list,), n_jobs=n_jobs, verbose=verbose)
# pbar = tqdm(
# total=(1 + len(Gn)) * len(Gn) / 2,
@@ -226,7 +234,7 @@ def _se_do(A_wave1, A_wave2, lmda):

###############################################################################
def _conjugate_gradient(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
node_label, edge_label, eweight, n_jobs):
node_label, edge_label, eweight, n_jobs, verbose=True):
"""Calculate walk graph kernels up to n between 2 graphs using conjugate method.

Parameters
@@ -265,8 +273,8 @@ def _conjugate_gradient(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
# else:
# reindex nodes using consecutive integers for convenience of kernel calculation.
Gn = [nx.convert_node_labels_to_integers(
g, first_label=0, label_attribute='label_orignal') for g in tqdm(
Gn, desc='reindex vertices', file=sys.stdout)]
g, first_label=0, label_attribute='label_orignal') for g in (tqdm(
Gn, desc='reindex vertices', file=sys.stdout) if verbose else Gn)]
if p == None and q == None: # p and q are uniform distributions as default.
def init_worker(gn_toshare):
@@ -275,7 +283,7 @@ def _conjugate_gradient(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
do_partial = partial(wrapper_cg_labled_do, ds_attrs, node_kernels,
node_label, edge_kernels, edge_label, lmda)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(Gn,), n_jobs=n_jobs)
glbv=(Gn,), n_jobs=n_jobs, verbose=verbose)
# pbar = tqdm(
# total=(1 + len(Gn)) * len(Gn) / 2,
@@ -341,7 +349,7 @@ def _cg_labled_do(g1, g2, ds_attrs, node_kernels, node_label,

###############################################################################
def _fixed_point(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
node_label, edge_label, eweight, n_jobs):
node_label, edge_label, eweight, n_jobs, verbose=True):
"""Calculate walk graph kernels up to n between 2 graphs using Fixed-Point method.

Parameters
@@ -393,8 +401,8 @@ def _fixed_point(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
# else:
# reindex nodes using consecutive integers for convenience of kernel calculation.
Gn = [nx.convert_node_labels_to_integers(
g, first_label=0, label_attribute='label_orignal') for g in tqdm(
Gn, desc='reindex vertices', file=sys.stdout)]
g, first_label=0, label_attribute='label_orignal') for g in (tqdm(
Gn, desc='reindex vertices', file=sys.stdout) if verbose else Gn)]
if p == None and q == None: # p and q are uniform distributions as default.
def init_worker(gn_toshare):
@@ -403,7 +411,7 @@ def _fixed_point(Gn, lmda, p, q, ds_attrs, node_kernels, edge_kernels,
do_partial = partial(wrapper_fp_labled_do, ds_attrs, node_kernels,
node_label, edge_kernels, edge_label, lmda)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(Gn,), n_jobs=n_jobs)
glbv=(Gn,), n_jobs=n_jobs, verbose=verbose)
return Kmatrix


@@ -445,7 +453,7 @@ def func_fp(x, p_times, lmda, w_times):


###############################################################################
def _spectral_decomposition(Gn, weight, p, q, sub_kernel, eweight, n_jobs):
def _spectral_decomposition(Gn, weight, p, q, sub_kernel, eweight, n_jobs, verbose=True):
"""Calculate walk graph kernels up to n between 2 unlabeled graphs using
spectral decomposition method. Labels will be ignored.

@@ -469,7 +477,8 @@ def _spectral_decomposition(Gn, weight, p, q, sub_kernel, eweight, n_jobs):
# precompute the spectral decomposition of each graph.
P_list = []
D_list = []
for G in tqdm(Gn, desc='spectral decompose', file=sys.stdout):
for G in (tqdm(Gn, desc='spectral decompose', file=sys.stdout) if
verbose else Gn):
# don't normalize adjacency matrices if q is a uniform vector. Note
# A accually is the transpose of the adjacency matrix.
A = nx.adjacency_matrix(G, eweight).todense().transpose()
@@ -488,7 +497,8 @@ def _spectral_decomposition(Gn, weight, p, q, sub_kernel, eweight, n_jobs):
G_D = D_toshare
do_partial = partial(wrapper_sd_do, weight, sub_kernel)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(q_T_list, P_list, D_list), n_jobs=n_jobs)
glbv=(q_T_list, P_list, D_list), n_jobs=n_jobs,
verbose=verbose)
# pbar = tqdm(


+ 23
- 15
pygraph/kernels/spKernel.py View File

@@ -56,7 +56,8 @@ def spkernel(*args,
Gn = args[0] if len(args) == 1 else [args[0], args[1]]
weight = None
if edge_weight is None:
print('\n None edge weight specified. Set all weight to 1.\n')
if verbose:
print('\n None edge weight specified. Set all weight to 1.\n')
else:
try:
some_weight = list(
@@ -64,13 +65,15 @@ def spkernel(*args,
if isinstance(some_weight, (float, int)):
weight = edge_weight
else:
print(
'\n Edge weight with name %s is not float or integer. Set all weight to 1.\n'
% edge_weight)
if verbose:
print(
'\n Edge weight with name %s is not float or integer. Set all weight to 1.\n'
% edge_weight)
except:
print(
'\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n'
% edge_weight)
if verbose:
print(
'\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n'
% edge_weight)
ds_attrs = get_dataset_attributes(
Gn,
attr_names=['node_labeled', 'node_attr_dim', 'is_directed'],
@@ -83,8 +86,9 @@ def spkernel(*args,
idx = [G[0] for G in Gn]
Gn = [G[1] for G in Gn]
if len(Gn) != len_gn:
print('\n %d graphs are removed as they don\'t contain edges.\n' %
(len_gn - len(Gn)))
if verbose:
print('\n %d graphs are removed as they don\'t contain edges.\n' %
(len_gn - len(Gn)))

start_time = time.time()

@@ -100,9 +104,12 @@ def spkernel(*args,
chunksize = int(len(Gn) / n_jobs) + 1
else:
chunksize = 100
for i, g in tqdm(
pool.imap_unordered(getsp_partial, itr, chunksize),
desc='getting sp graphs', file=sys.stdout):
if verbose:
iterator = tqdm(pool.imap_unordered(getsp_partial, itr, chunksize),
desc='getting sp graphs', file=sys.stdout)
else:
iterator = pool.imap_unordered(getsp_partial, itr, chunksize)
for i, g in iterator:
Gn[i] = g
pool.close()
pool.join()
@@ -186,9 +193,10 @@ def spkernel(*args,
# Kmatrix[j][i] = kernel

run_time = time.time() - start_time
print(
"\n --- shortest path kernel matrix of size %d built in %s seconds ---"
% (len(Gn), run_time))
if verbose:
print(
"\n --- shortest path kernel matrix of size %d built in %s seconds ---"
% (len(Gn), run_time))

return Kmatrix, run_time, idx



+ 24
- 18
pygraph/kernels/structuralspKernel.py View File

@@ -32,7 +32,8 @@ def structuralspkernel(*args,
node_kernels=None,
edge_kernels=None,
compute_method='naive',
n_jobs=None):
n_jobs=None,
verbose=True):
"""Calculate mean average structural shortest path kernels between graphs.

Parameters
@@ -75,7 +76,8 @@ def structuralspkernel(*args,
Gn = args[0] if len(args) == 1 else [args[0], args[1]]
weight = None
if edge_weight is None:
print('\n None edge weight specified. Set all weight to 1.\n')
if verbose:
print('\n None edge weight specified. Set all weight to 1.\n')
else:
try:
some_weight = list(
@@ -83,13 +85,15 @@ def structuralspkernel(*args,
if isinstance(some_weight, (float, int)):
weight = edge_weight
else:
print(
'\n Edge weight with name %s is not float or integer. Set all weight to 1.\n'
% edge_weight)
if verbose:
print(
'\n Edge weight with name %s is not float or integer. Set all weight to 1.\n'
% edge_weight)
except:
print(
'\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n'
% edge_weight)
if verbose:
print(
'\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n'
% edge_weight)
ds_attrs = get_dataset_attributes(
Gn,
attr_names=['node_labeled', 'node_attr_dim', 'edge_labeled',
@@ -110,11 +114,13 @@ def structuralspkernel(*args,
if compute_method == 'trie':
getsp_partial = partial(wrapper_getSP_trie, weight, ds_attrs['is_directed'])
else:
getsp_partial = partial(wrapper_getSP_naive, weight, ds_attrs['is_directed'])
for i, sp in tqdm(
pool.imap_unordered(getsp_partial, itr, chunksize),
desc='getting shortest paths',
file=sys.stdout):
getsp_partial = partial(wrapper_getSP_naive, weight, ds_attrs['is_directed'])
if verbose:
iterator = tqdm(pool.imap_unordered(getsp_partial, itr, chunksize),
desc='getting shortest paths', file=sys.stdout)
else:
iterator = pool.imap_unordered(getsp_partial, itr, chunksize)
for i, sp in iterator:
splist[i] = sp
# time.sleep(10)
pool.close()
@@ -169,12 +175,12 @@ def structuralspkernel(*args,
do_partial = partial(wrapper_ssp_do_trie, ds_attrs, node_label, edge_label,
node_kernels, edge_kernels)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(splist, Gn), n_jobs=n_jobs)
glbv=(splist, Gn), n_jobs=n_jobs, verbose=verbose)
else:
do_partial = partial(wrapper_ssp_do, ds_attrs, node_label, edge_label,
node_kernels, edge_kernels)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(splist, Gn), n_jobs=n_jobs)
glbv=(splist, Gn), n_jobs=n_jobs, verbose=verbose)
# # ---- use pool.map to parallel. ----
# pool = Pool(n_jobs)
@@ -233,9 +239,9 @@ def structuralspkernel(*args,
# Kmatrix[j][i] = kernel

run_time = time.time() - start_time
print(
"\n --- shortest path kernel matrix of size %d built in %s seconds ---"
% (len(Gn), run_time))
if verbose:
print("\n --- shortest path kernel matrix of size %d built in %s seconds ---"
% (len(Gn), run_time))

return Kmatrix, run_time



+ 13
- 9
pygraph/kernels/untilHPathKernel.py View File

@@ -28,7 +28,8 @@ def untilhpathkernel(*args,
depth=10,
k_func='tanimoto',
compute_method='trie',
n_jobs=None):
n_jobs=None,
verbose=True):
"""Calculate path graph kernels up to depth/hight h between graphs.
Parameters
----------
@@ -88,9 +89,12 @@ def untilhpathkernel(*args,
else:
getps_partial = partial(wrapper_find_all_paths_until_length, depth,
ds_attrs, node_label, edge_label)
for i, ps in tqdm(
pool.imap_unordered(getps_partial, itr, chunksize),
desc='getting paths', file=sys.stdout):
if verbose:
iterator = tqdm(pool.imap_unordered(getps_partial, itr, chunksize),
desc='getting paths', file=sys.stdout)
else:
iterator = pool.imap_unordered(getps_partial, itr, chunksize)
for i, ps in iterator:
all_paths[i] = ps
pool.close()
pool.join()
@@ -122,14 +126,14 @@ def untilhpathkernel(*args,
G_trie = trie_toshare
do_partial = partial(wrapper_uhpath_do_trie, k_func)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(all_paths,), n_jobs=n_jobs)
glbv=(all_paths,), n_jobs=n_jobs, verbose=verbose)
else:
def init_worker(plist_toshare):
global G_plist
G_plist = plist_toshare
do_partial = partial(wrapper_uhpath_do_naive, k_func)
parallel_gm(do_partial, Kmatrix, Gn, init_worker=init_worker,
glbv=(all_paths,), n_jobs=n_jobs)
glbv=(all_paths,), n_jobs=n_jobs, verbose=verbose)
# # ---- direct running, normally use single CPU core. ----
@@ -167,9 +171,9 @@ def untilhpathkernel(*args,
# pbar.update(1)

run_time = time.time() - start_time
print(
"\n --- kernel matrix of path kernel up to %d of size %d built in %s seconds ---"
% (depth, len(Gn), run_time))
if verbose:
print("\n --- kernel matrix of path kernel up to %d of size %d built in %s seconds ---"
% (depth, len(Gn), run_time))

# print(Kmatrix[0][0:10])
return Kmatrix, run_time


+ 116
- 78
pygraph/utils/model_selection_precomputed.py View File

@@ -32,7 +32,8 @@ def model_selection_for_precomputed_kernel(datafile,
extra_params=None,
ds_name='ds-unknown',
n_jobs=1,
read_gm_from_file=False):
read_gm_from_file=False,
verbose=True):
"""Perform model selection, fitting and testing for precomputed kernels using nested cv. Print out neccessary data during the process then finally the results.

Parameters
@@ -84,15 +85,17 @@ def model_selection_for_precomputed_kernel(datafile,
raise Exception(
'The model type is incorrect! Please choose from regression or classification.'
)
print()
print('--- This is a %s problem ---' % model_type)
if verbose:
print()
print('--- This is a %s problem ---' % model_type)
str_fw += 'This is a %s problem.\n' % model_type
# calculate gram matrices rather than read them from file.
if read_gm_from_file == False:
# Load the dataset
print()
print('\n1. Loading dataset from file...')
if verbose:
print()
print('\n1. Loading dataset from file...')
if isinstance(datafile, str):
dataset, y_all = loadDataset(
datafile, filename_y=datafile_y, extra_params=extra_params)
@@ -117,14 +120,16 @@ def model_selection_for_precomputed_kernel(datafile,
] # list to store param grids precomputed ignoring the useless ones
# calculate all gram matrices
print()
print('2. Calculating gram matrices. This could take a while...')
if verbose:
print()
print('2. Calculating gram matrices. This could take a while...')
str_fw += '\nII. Gram matrices.\n\n'
tts = time.time() # start training time
nb_gm_ignore = 0 # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN)
for idx, params_out in enumerate(param_list_precomputed):
y = y_all[:]
params_out['n_jobs'] = n_jobs
params_out['verbose'] = verbose
# print(dataset)
# import networkx as nx
# nx.draw_networkx(dataset[1])
@@ -154,23 +159,27 @@ def model_selection_for_precomputed_kernel(datafile,
for j in range(i, len(Kmatrix)):
Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])
Kmatrix[j][i] = Kmatrix[i][j]
print()
if verbose:
print()
if params_out == {}:
print('the gram matrix is: ')
if verbose:
print('the gram matrix is: ')
str_fw += 'the gram matrix is:\n\n'
else:
print('the gram matrix with parameters', params_out, 'is: \n\n')
if verbose:
print('the gram matrix with parameters', params_out, 'is: \n\n')
str_fw += 'the gram matrix with parameters %s is:\n\n' % params_out
if len(Kmatrix) < 2:
nb_gm_ignore += 1
print('ignored, as at most only one of all its diagonal value is non-zero.')
if verbose:
print('ignored, as at most only one of all its diagonal value is non-zero.')
str_fw += 'ignored, as at most only one of all its diagonal value is non-zero.\n\n'
else:
if np.isnan(Kmatrix).any(
): # if the matrix contains elements that are not numbers
nb_gm_ignore += 1
print('ignored, as it contains elements that are not numbers.')
if verbose:
print('ignored, as it contains elements that are not numbers.')
str_fw += 'ignored, as it contains elements that are not numbers.\n\n'
else:
# print(Kmatrix)
@@ -193,10 +202,12 @@ def model_selection_for_precomputed_kernel(datafile,
gram_matrix_time.append(current_run_time)
param_list_pre_revised.append(params_out)
if nb_g_ignore > 0:
print(', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore)
if verbose:
print(', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore)
str_fw += ', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore
print()
print(
if verbose:
print()
print(
'{} gram matrices are calculated, {} of which are ignored.'.format(
len(param_list_precomputed), nb_gm_ignore))
str_fw += '{} gram matrices are calculated, {} of which are ignored.\n\n'.format(len(param_list_precomputed), nb_gm_ignore)
@@ -205,20 +216,22 @@ def model_selection_for_precomputed_kernel(datafile,
'{}: {}\n'.format(idx, params_out)
for idx, params_out in enumerate(param_list_precomputed)
])
print()

if verbose:
print()
if len(gram_matrices) == 0:
print('all gram matrices are ignored, no results obtained.')
if verbose:
print('all gram matrices are ignored, no results obtained.')
str_fw += '\nall gram matrices are ignored, no results obtained.\n\n'
else:
# save gram matrices to file.
np.savez(results_dir + '/' + ds_name + '.gm',
gms=gram_matrices, params=param_list_pre_revised, y=y,
gmtime=gram_matrix_time)
print(
if verbose:
print(
'3. Fitting and predicting using nested cross validation. This could really take a while...'
)
)
# ---- use pool.imap_unordered to parallel and track progress. ----
# train_pref = []
@@ -252,7 +265,12 @@ def model_selection_for_precomputed_kernel(datafile,
# else:
# chunksize = 1000
chunksize = 1
for o1, o2, o3 in tqdm(pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout):
if verbose:
iterator = tqdm(pool.imap_unordered(trial_do_partial,
range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout)
else:
iterator = pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize)
for o1, o2, o3 in iterator:
train_pref.append(o1)
val_pref.append(o2)
test_pref.append(o3)
@@ -278,8 +296,9 @@ def model_selection_for_precomputed_kernel(datafile,
# test_pref.append(o3)
# print()
print()
print('4. Getting final performance...')
if verbose:
print()
print('4. Getting final performance...')
str_fw += '\nIII. Performance.\n\n'
# averages and confidences of performances on outer trials for each combination of parameters
average_train_scores = np.mean(train_pref, axis=0)
@@ -311,11 +330,12 @@ def model_selection_for_precomputed_kernel(datafile,
param_list_pre_revised[i] for i in best_params_index[0]
]
best_params_in = [param_list[i] for i in best_params_index[1]]
print('best_params_out: ', best_params_out)
print('best_params_in: ', best_params_in)
print()
print('best_val_perf: ', best_val_perf)
print('best_val_std: ', min_val_std)
if verbose:
print('best_params_out: ', best_params_out)
print('best_params_in: ', best_params_in)
print()
print('best_val_perf: ', best_val_perf)
print('best_val_std: ', min_val_std)
str_fw += 'best settings of hyper-params to build gram matrix: %s\n' % best_params_out
str_fw += 'best settings of other hyper-params: %s\n\n' % best_params_in
str_fw += 'best_val_perf: %s\n' % best_val_perf
@@ -332,8 +352,9 @@ def model_selection_for_precomputed_kernel(datafile,
std_perf_scores[value][best_params_index[1][idx]]
for idx, value in enumerate(best_params_index[0])
]
print('final_performance: ', final_performance)
print('final_confidence: ', final_confidence)
if verbose:
print('final_performance: ', final_performance)
print('final_confidence: ', final_confidence)
str_fw += 'final_performance: %s\n' % final_performance
str_fw += 'final_confidence: %s\n' % final_confidence
train_performance = [
@@ -344,28 +365,29 @@ def model_selection_for_precomputed_kernel(datafile,
std_train_scores[value][best_params_index[1][idx]]
for idx, value in enumerate(best_params_index[0])
]
print('train_performance: %s' % train_performance)
print('train_std: ', train_std)
if verbose:
print('train_performance: %s' % train_performance)
print('train_std: ', train_std)
str_fw += 'train_performance: %s\n' % train_performance
str_fw += 'train_std: %s\n\n' % train_std
print()

if verbose:
print()
tt_total = time.time() - tts # training time for all hyper-parameters
average_gram_matrix_time = np.mean(gram_matrix_time)
std_gram_matrix_time = np.std(gram_matrix_time, ddof=1)
std_gram_matrix_time = np.std(gram_matrix_time, ddof=1) if len(gram_matrix_time) > 1 else 0
best_gram_matrix_time = [
gram_matrix_time[i] for i in best_params_index[0]
]
ave_bgmt = np.mean(best_gram_matrix_time)
std_bgmt = np.std(best_gram_matrix_time, ddof=1)
print(
'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'
.format(average_gram_matrix_time, std_gram_matrix_time))
print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format(
ave_bgmt, std_bgmt))
print(
'total training time with all hyper-param choices: {:.2f}s'.format(
tt_total))
std_bgmt = np.std(best_gram_matrix_time, ddof=1) if len(best_gram_matrix_time) > 1 else 0
if verbose:
print('time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'
.format(average_gram_matrix_time, std_gram_matrix_time))
print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format(
ave_bgmt, std_bgmt))
print('total training time with all hyper-param choices: {:.2f}s'.format(
tt_total))
str_fw += 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s\n'.format(average_gram_matrix_time, std_gram_matrix_time)
str_fw += 'time to calculate best gram matrix: {:.2f}±{:.2f}s\n'.format(ave_bgmt, std_bgmt)
str_fw += 'total training time with all hyper-param choices: {:.2f}s\n\n'.format(tt_total)
@@ -437,7 +459,8 @@ def model_selection_for_precomputed_kernel(datafile,
'params', 'train_perf', 'valid_perf', 'test_perf',
'gram_matrix_time'
]
print()
if verbose:
print()
tb_print = tabulate(
OrderedDict(
sorted(table_dict.items(),
@@ -453,8 +476,9 @@ def model_selection_for_precomputed_kernel(datafile,
param_list = list(ParameterGrid(param_grid))
# read gram matrices from file.
print()
print('2. Reading gram matrices from file...')
if verbose:
print()
print('2. Reading gram matrices from file...')
str_fw += '\nII. Gram matrices.\n\nGram matrices are read from file, see last log for detail.\n'
gmfile = np.load(results_dir + '/' + ds_name + '.gm.npz')
gram_matrices = gmfile['gms'] # a list to store gram matrices for all param_grid_precomputed
@@ -464,9 +488,10 @@ def model_selection_for_precomputed_kernel(datafile,
tts = time.time() # start training time
# nb_gm_ignore = 0 # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN)
print(
'3. Fitting and predicting using nested cross validation. This could really take a while...'
)
if verbose:
print(
'3. Fitting and predicting using nested cross validation. This could really take a while...'
)
# ---- use pool.imap_unordered to parallel and track progress. ----
def init_worker(gms_toshare):
@@ -479,7 +504,12 @@ def model_selection_for_precomputed_kernel(datafile,
val_pref = []
test_pref = []
chunksize = 1
for o1, o2, o3 in tqdm(pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout):
if verbose:
iterator = tqdm(pool.imap_unordered(trial_do_partial,
range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout)
else:
iterator = pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize)
for o1, o2, o3 in iterator:
train_pref.append(o1)
val_pref.append(o2)
test_pref.append(o3)
@@ -509,8 +539,9 @@ def model_selection_for_precomputed_kernel(datafile,
# val_pref.append(o2)
# test_pref.append(o3)

print()
print('4. Getting final performance...')
if verbose:
print()
print('4. Getting final performance...')
str_fw += '\nIII. Performance.\n\n'
# averages and confidences of performances on outer trials for each combination of parameters
average_train_scores = np.mean(train_pref, axis=0)
@@ -537,11 +568,12 @@ def model_selection_for_precomputed_kernel(datafile,
param_list_pre_revised[i] for i in best_params_index[0]
]
best_params_in = [param_list[i] for i in best_params_index[1]]
print('best_params_out: ', best_params_out)
print('best_params_in: ', best_params_in)
print()
print('best_val_perf: ', best_val_perf)
print('best_val_std: ', min_val_std)
if verbose:
print('best_params_out: ', best_params_out)
print('best_params_in: ', best_params_in)
print()
print('best_val_perf: ', best_val_perf)
print('best_val_std: ', min_val_std)
str_fw += 'best settings of hyper-params to build gram matrix: %s\n' % best_params_out
str_fw += 'best settings of other hyper-params: %s\n\n' % best_params_in
str_fw += 'best_val_perf: %s\n' % best_val_perf
@@ -555,8 +587,9 @@ def model_selection_for_precomputed_kernel(datafile,
std_perf_scores[value][best_params_index[1][idx]]
for idx, value in enumerate(best_params_index[0])
]
print('final_performance: ', final_performance)
print('final_confidence: ', final_confidence)
if verbose:
print('final_performance: ', final_performance)
print('final_confidence: ', final_confidence)
str_fw += 'final_performance: %s\n' % final_performance
str_fw += 'final_confidence: %s\n' % final_confidence
train_performance = [
@@ -567,30 +600,34 @@ def model_selection_for_precomputed_kernel(datafile,
std_train_scores[value][best_params_index[1][idx]]
for idx, value in enumerate(best_params_index[0])
]
print('train_performance: %s' % train_performance)
print('train_std: ', train_std)
if verbose:
print('train_performance: %s' % train_performance)
print('train_std: ', train_std)
str_fw += 'train_performance: %s\n' % train_performance
str_fw += 'train_std: %s\n\n' % train_std

print()
if verbose:
print()
average_gram_matrix_time = np.mean(gram_matrix_time)
std_gram_matrix_time = np.std(gram_matrix_time, ddof=1)
std_gram_matrix_time = np.std(gram_matrix_time, ddof=1) if len(gram_matrix_time) > 1 else 0
best_gram_matrix_time = [
gram_matrix_time[i] for i in best_params_index[0]
]
ave_bgmt = np.mean(best_gram_matrix_time)
std_bgmt = np.std(best_gram_matrix_time, ddof=1)
print(
'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'
.format(average_gram_matrix_time, std_gram_matrix_time))
print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format(
ave_bgmt, std_bgmt))
std_bgmt = np.std(best_gram_matrix_time, ddof=1) if len(best_gram_matrix_time) > 1 else 0
if verbose:
print(
'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'
.format(average_gram_matrix_time, std_gram_matrix_time))
print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format(
ave_bgmt, std_bgmt))
tt_poster = time.time() - tts # training time with hyper-param choices who did not participate in calculation of gram matrices
print(
'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s'.format(
tt_poster))
print('total training time with all hyper-param choices: {:.2f}s'.format(
tt_poster + np.sum(gram_matrix_time)))
if verbose:
print(
'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s'.format(
tt_poster))
print('total training time with all hyper-param choices: {:.2f}s'.format(
tt_poster + np.sum(gram_matrix_time)))
# str_fw += 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s\n'.format(average_gram_matrix_time, std_gram_matrix_time)
# str_fw += 'time to calculate best gram matrix: {:.2f}±{:.2f}s\n'.format(ave_bgmt, std_bgmt)
str_fw += 'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s\n\n'.format(tt_poster)
@@ -633,7 +670,8 @@ def model_selection_for_precomputed_kernel(datafile,
keyorder = [
'params', 'train_perf', 'valid_perf', 'test_perf'
]
print()
if verbose:
print()
tb_print = tabulate(
OrderedDict(
sorted(table_dict.items(),


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