New translations compute_graph_kernel.py (Chinese Simplified)

lang/zh/gklearn/examples/kernels/compute_graph_kernel.py

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+# -*- coding: utf-8 -*-
+"""compute_graph_kernel.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/17Q2QCl9CAtDweGF8LiWnWoN2laeJqT0u
+
+**This script demonstrates how to compute a graph kernel.**
+---
+
+**0.   Install `graphkit-learn`.**
+"""
+
+"""**1.   Get dataset.**"""
+
+from gklearn.utils import Dataset
+
+# Predefined dataset name, use dataset "MUTAG".
+ds_name = 'MUTAG'
+
+# Initialize a Dataset.
+dataset = Dataset()
+# Load predefined dataset "MUTAG".
+dataset.load_predefined_dataset(ds_name)
+len(dataset.graphs)
+
+"""**2.  Compute graph kernel.**"""
+
+from gklearn.kernels import PathUpToH
+
+# Initailize parameters for graph kernel computation.
+kernel_options = {'depth': 3,
+			      		  'k_func': 'MinMax',
+					        'compute_method': 'trie'
+								 }
+
+# Initialize graph kernel.
+graph_kernel = PathUpToH(node_labels=dataset.node_labels, # list of node label names.
+						 edge_labels=dataset.edge_labels, # list of edge label names.
+						 ds_infos=dataset.get_dataset_infos(keys=['directed']), # dataset information required for computation.
+						 **kernel_options, # options for computation.
+						 )
+
+print('done.')
+
+import multiprocessing
+import matplotlib.pyplot as plt
+
+# Compute Gram matrix.
+gram_matrix, run_time = graph_kernel.compute(dataset.graphs,
+											 parallel='imap_unordered', # or None.
+											 n_jobs=multiprocessing.cpu_count(), # number of parallel jobs.
+											 normalize=True, # whether to return normalized Gram matrix.
+											 verbose=2 # whether to print out results.
+											 )
+# Print results.
+print()
+print(gram_matrix)
+print(run_time)
+plt.imshow(gram_matrix)
+
+import multiprocessing
+
+# Compute grah kernels between a graph and a list of graphs.
+kernel_list, run_time = graph_kernel.compute(dataset.graphs, # a list of graphs. 
+                                             dataset.graphs[0], # a single graph.
+											 parallel='imap_unordered', # or None.
+											 n_jobs=multiprocessing.cpu_count(), # number of parallel jobs.
+											 verbose=2 # whether to print out results.
+                                            )
+# Print results.
+print()
+print(kernel_list)
+print(run_time)
+
+import multiprocessing
+
+# Compute a grah kernel between two graphs.
+kernel, run_time = graph_kernel.compute(dataset.graphs[0], # a single graph. 
+                                        dataset.graphs[1], # another single graph.
+										verbose=2 # whether to print out results.
+                                       )
+# Print results.
+print()
+print(kernel)
+print(run_time)