linlin
5 years ago
1 changed files with 8 additions and 8 deletions
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+ 8
- 8
lang/fr/gklearn/kernels/treeletKernel.py
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| @@ -29,15 +29,15 @@ def treeletkernel(*args, | |||||
| n_jobs=None, | n_jobs=None, | ||||
| chunksize=None, | chunksize=None, | ||||
| verbose=True): | verbose=True): | ||||
| """Calculate treelet graph kernels between graphs. | |||||
| """Compute treelet graph kernels between graphs. | |||||
| Parameters | Parameters | ||||
| ---------- | ---------- | ||||
| Gn : List of NetworkX graph | Gn : List of NetworkX graph | ||||
| List of graphs between which the kernels are calculated. | |||||
| List of graphs between which the kernels are computed. | |||||
| G1, G2 : NetworkX graphs | G1, G2 : NetworkX graphs | ||||
| Two graphs between which the kernel is calculated. | |||||
| Two graphs between which the kernel is computed. | |||||
| sub_kernel : function | sub_kernel : function | ||||
| The sub-kernel between 2 real number vectors. Each vector counts the | The sub-kernel between 2 real number vectors. Each vector counts the | ||||
| @@ -89,7 +89,7 @@ def treeletkernel(*args, | |||||
| # ---- use pool.imap_unordered to parallel and track progress. ---- | # ---- use pool.imap_unordered to parallel and track progress. ---- | ||||
| if parallel == 'imap_unordered': | if parallel == 'imap_unordered': | ||||
| # get all canonical keys of all graphs before calculating kernels to save | |||||
| # get all canonical keys of all graphs before computing kernels to save | |||||
| # time, but this may cost a lot of memory for large dataset. | # time, but this may cost a lot of memory for large dataset. | ||||
| pool = Pool(n_jobs) | pool = Pool(n_jobs) | ||||
| itr = zip(Gn, range(0, len(Gn))) | itr = zip(Gn, range(0, len(Gn))) | ||||
| @@ -120,8 +120,8 @@ def treeletkernel(*args, | |||||
| glbv=(canonkeys,), n_jobs=n_jobs, chunksize=chunksize, verbose=verbose) | glbv=(canonkeys,), n_jobs=n_jobs, chunksize=chunksize, verbose=verbose) | ||||
| # ---- do not use parallelization. ---- | # ---- do not use parallelization. ---- | ||||
| elif parallel == None: | |||||
| # get all canonical keys of all graphs before calculating kernels to save | |||||
| elif parallel is None: | |||||
| # get all canonical keys of all graphs before computing kernels to save | |||||
| # time, but this may cost a lot of memory for large dataset. | # time, but this may cost a lot of memory for large dataset. | ||||
| canonkeys = [] | canonkeys = [] | ||||
| for g in (tqdm(Gn, desc='getting canonkeys', file=sys.stdout) if verbose else Gn): | for g in (tqdm(Gn, desc='getting canonkeys', file=sys.stdout) if verbose else Gn): | ||||
| @@ -148,7 +148,7 @@ def treeletkernel(*args, | |||||
| def _treeletkernel_do(canonkey1, canonkey2, sub_kernel): | def _treeletkernel_do(canonkey1, canonkey2, sub_kernel): | ||||
| """Calculate treelet graph kernel between 2 graphs. | |||||
| """Compute treelet graph kernel between 2 graphs. | |||||
| Parameters | Parameters | ||||
| ---------- | ---------- | ||||
| @@ -210,7 +210,7 @@ def get_canonkeys(G, node_label, edge_label, labeled, is_directed): | |||||
| # n-star patterns | # n-star patterns | ||||
| patterns['3star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 3] | patterns['3star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 3] | ||||
| patterns['4star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 4] | |||||
| patterns['4star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 4] # @todo: check self loop. | |||||
| patterns['5star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 5] | patterns['5star'] = [[node] + [neighbor for neighbor in G[node]] for node in G.nodes() if G.degree(node) == 5] | ||||
| # n-star patterns | # n-star patterns | ||||
| canonkey['6'] = len(patterns['3star']) | canonkey['6'] = len(patterns['3star']) | ||||