New translations test_graphkernels.py (Chinese Simplified)

lang/zh/gklearn/tests/test_graphkernels.py

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+"""Tests of graph kernels.
+"""
+
+import pytest
+import multiprocessing
+
+
+def chooseDataset(ds_name):
+    """Choose dataset according to name.
+    """
+    from gklearn.utils.graphfiles import loadDataset
+
+    # no node labels (and no edge labels).
+    if ds_name == 'Alkane':
+        ds_file = 'datasets/Alkane/dataset.ds'
+        ds_y = 'datasets/Alkane/dataset_boiling_point_names.txt'
+        Gn, y = loadDataset(ds_file, filename_y=ds_y)
+        for G in Gn:
+            for node in G.nodes:
+                del G.nodes[node]['attributes']
+    # node symbolic labels.
+    elif ds_name == 'Acyclic':
+        ds_file = 'datasets/acyclic/dataset_bps.ds'
+        Gn, y = loadDataset(ds_file)
+        for G in Gn:
+            for node in G.nodes:
+                del G.nodes[node]['attributes']
+    # node non-symbolic labels.
+    elif ds_name == 'Letter-med':
+        ds_file = 'datasets/Letter-med/Letter-med_A.txt'
+        Gn, y = loadDataset(ds_file)
+    # node symbolic and non-symbolic labels (and edge symbolic labels).
+    elif ds_name == 'AIDS':
+        ds_file = 'datasets/AIDS/AIDS_A.txt'
+        Gn, y = loadDataset(ds_file)
+    
+    # edge non-symbolic labels (no node labels).
+    elif ds_name == 'Fingerprint_edge':
+        import networkx as nx
+        ds_file = 'datasets/Fingerprint/Fingerprint_A.txt'
+        Gn, y = loadDataset(ds_file)
+        Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_edges(G) != 0]
+        idx = [G[0] for G in Gn]
+        Gn = [G[1] for G in Gn]
+        y = [y[i] for i in idx]
+        for G in Gn:
+            G.graph['node_attrs'] = []
+            for node in G.nodes:
+                del G.nodes[node]['attributes']
+                del G.nodes[node]['x']
+                del G.nodes[node]['y']
+    # edge non-symbolic labels (and node non-symbolic labels).
+    elif ds_name == 'Fingerprint':
+        import networkx as nx
+        ds_file = 'datasets/Fingerprint/Fingerprint_A.txt'
+        Gn, y = loadDataset(ds_file)
+        Gn = [(idx, G) for idx, G in enumerate(Gn) if nx.number_of_edges(G) != 0]
+        idx = [G[0] for G in Gn]
+        Gn = [G[1] for G in Gn]
+        y = [y[i] for i in idx]
+    # edge symbolic and non-symbolic labels (and node symbolic and non-symbolic labels).
+    elif ds_name == 'Cuneiform':
+        import networkx as nx
+        ds_file = 'datasets/Cuneiform/Cuneiform_A.txt'
+        Gn, y = loadDataset(ds_file)
+    
+    Gn = Gn[0:3]
+    y = y[0:3]
+    
+    return Gn, y
+
+
+@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS'])
+@pytest.mark.parametrize('weight,compute_method', [(0.01, 'geo'), (1, 'exp')])
+#@pytest.mark.parametrize('parallel', ['imap_unordered', None])
+def test_commonwalkkernel(ds_name, weight, compute_method):
+    """Test common walk kernel.
+    """
+    from gklearn.kernels.commonWalkKernel import commonwalkkernel
+    
+    Gn, y = chooseDataset(ds_name)
+
+    try:
+        Kmatrix, run_time, idx = commonwalkkernel(Gn, 
+                                             node_label='atom', 
+                                             edge_label='bond_type',
+                                             weight=weight,
+                                             compute_method=compute_method,
+#                                             parallel=parallel,
+                                             n_jobs=multiprocessing.cpu_count(), 
+                                             verbose=True)
+    except Exception as exception:
+        assert False, exception
+        
+        
+@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS'])
+@pytest.mark.parametrize('remove_totters', [True, False])
+#@pytest.mark.parametrize('parallel', ['imap_unordered', None])
+def test_marginalizedkernel(ds_name, remove_totters):
+    """Test marginalized kernel.
+    """
+    from gklearn.kernels.marginalizedKernel import marginalizedkernel
+    
+    Gn, y = chooseDataset(ds_name)
+
+    try:
+        Kmatrix, run_time = marginalizedkernel(Gn, 
+                                               node_label='atom', 
+                                               edge_label='bond_type',
+                                               p_quit=0.5,
+                                               n_iteration=2,
+                                               remove_totters=remove_totters,
+#                                               parallel=parallel,
+                                               n_jobs=multiprocessing.cpu_count(), 
+                                               verbose=True)
+    except Exception as exception:
+        assert False, exception
+        
+        
+@pytest.mark.parametrize(
+        'compute_method,ds_name,sub_kernel',
+        [
+#            ('sylvester', 'Alkane', None),
+#            ('conjugate', 'Alkane', None),
+#            ('conjugate', 'AIDS', None),
+#            ('fp', 'Alkane', None),
+#            ('fp', 'AIDS', None),
+            ('spectral', 'Alkane', 'exp'),
+            ('spectral', 'Alkane', 'geo'),
+        ]
+)
+#@pytest.mark.parametrize('parallel', ['imap_unordered', None])
+def test_randomwalkkernel(ds_name, compute_method, sub_kernel):
+    """Test random walk kernel kernel.
+    """
+    from gklearn.kernels.randomWalkKernel import randomwalkkernel
+    from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
+    import functools
+    
+    Gn, y = chooseDataset(ds_name)
+
+    mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
+    sub_kernels = [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]
+    try:
+        Kmatrix, run_time, idx = randomwalkkernel(Gn,
+                                                  compute_method=compute_method,
+                                                  weight=1e-3,
+                                                  p=None,
+                                                  q=None,
+                                                  edge_weight=None,
+                                                  node_kernels=sub_kernels,
+                                                  edge_kernels=sub_kernels,
+                                                  node_label='atom', 
+                                                  edge_label='bond_type',
+                                                  sub_kernel=sub_kernel,
+#                                                  parallel=parallel,
+                                                 n_jobs=multiprocessing.cpu_count(), 
+                                                 verbose=True)
+    except Exception as exception:
+        assert False, exception
+
+        
+@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint'])
+#@pytest.mark.parametrize('parallel', ['imap_unordered', None])
+@pytest.mark.parametrize('parallel', ['imap_unordered'])
+def test_spkernel(ds_name, parallel):
+    """Test shortest path kernel.
+    """
+    from gklearn.kernels.spKernel import spkernel
+    from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
+    import functools
+    
+    Gn, y = chooseDataset(ds_name)
+    
+    mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
+    sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
+    try:
+        Kmatrix, run_time, idx = spkernel(Gn, node_label='atom', 
+            node_kernels=sub_kernels,
+            parallel=parallel, n_jobs=multiprocessing.cpu_count(), verbose=True)
+    except Exception as exception:
+        assert False, exception
+
+
+#@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint'])
+@pytest.mark.parametrize('ds_name', ['Alkane', 'Acyclic', 'Letter-med', 'AIDS', 'Fingerprint', 'Fingerprint_edge', 'Cuneiform'])
+@pytest.mark.parametrize('parallel', ['imap_unordered', None])
+def test_structuralspkernel(ds_name, parallel):
+    """Test structural shortest path kernel.
+    """
+    from gklearn.kernels.structuralspKernel import structuralspkernel
+    from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
+    import functools
+    
+    Gn, y = chooseDataset(ds_name)
+    
+    mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
+    sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
+    try:
+        Kmatrix, run_time = structuralspkernel(Gn, node_label='atom', 
+                      edge_label='bond_type', node_kernels=sub_kernels,
+                      edge_kernels=sub_kernels,
+                      parallel=parallel, n_jobs=multiprocessing.cpu_count(), 
+                      verbose=True)
+    except Exception as exception:
+        assert False, exception
+
+
+@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS'])
+@pytest.mark.parametrize('parallel', ['imap_unordered', None])
+#@pytest.mark.parametrize('k_func', ['MinMax', 'tanimoto', None])
+@pytest.mark.parametrize('k_func', ['MinMax', 'tanimoto'])
+@pytest.mark.parametrize('compute_method', ['trie', 'naive'])
+def test_untilhpathkernel(ds_name, parallel, k_func, compute_method):
+    """Test path kernel up to length $h$.
+    """
+    from gklearn.kernels.untilHPathKernel import untilhpathkernel
+    
+    Gn, y = chooseDataset(ds_name)
+    
+    try:
+        Kmatrix, run_time = untilhpathkernel(Gn, node_label='atom', 
+                          edge_label='bond_type',
+                          depth=2, k_func=k_func, compute_method=compute_method,
+                          parallel=parallel, 
+                          n_jobs=multiprocessing.cpu_count(), verbose=True)
+    except Exception as exception:
+        assert False, exception
+    
+    
+@pytest.mark.parametrize('ds_name', ['Alkane', 'AIDS'])
+@pytest.mark.parametrize('parallel', ['imap_unordered', None])
+def test_treeletkernel(ds_name, parallel):
+    """Test treelet kernel.
+    """
+    from gklearn.kernels.treeletKernel import treeletkernel
+    from gklearn.utils.kernels import polynomialkernel
+    import functools
+    
+    Gn, y = chooseDataset(ds_name)
+
+    pkernel = functools.partial(polynomialkernel, d=2, c=1e5)    
+    try:
+        Kmatrix, run_time = treeletkernel(Gn, 
+                                          sub_kernel=pkernel,
+                                          node_label='atom', 
+                                          edge_label='bond_type',
+                                          parallel=parallel,
+                                          n_jobs=multiprocessing.cpu_count(), 
+                                          verbose=True)
+    except Exception as exception:
+        assert False, exception
+        
+        
+@pytest.mark.parametrize('ds_name', ['Acyclic'])
+#@pytest.mark.parametrize('base_kernel', ['subtree', 'sp', 'edge'])
+@pytest.mark.parametrize('base_kernel', ['subtree'])
+@pytest.mark.parametrize('parallel', ['imap_unordered', None])
+def test_weisfeilerlehmankernel(ds_name, parallel, base_kernel):
+    """Test Weisfeiler-Lehman kernel.
+    """
+    from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel
+    
+    Gn, y = chooseDataset(ds_name)
+
+    try:
+        Kmatrix, run_time = weisfeilerlehmankernel(Gn, 
+                                                   node_label='atom', 
+                                                   edge_label='bond_type',
+                                                   height=2,
+                                                   base_kernel=base_kernel,
+                                                   parallel=parallel,
+                                                   n_jobs=multiprocessing.cpu_count(), 
+                                                   verbose=True)
+    except Exception as exception:
+        assert False, exception
+        
+
+if __name__ == "__main__":
+#    test_spkernel('Alkane', 'imap_unordered')
+    test_structuralspkernel('Fingerprint_edge', 'imap_unordered')