Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/node_classification/lgc.py @ 0:4f3585e2f14b draft default tip
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author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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1 """Function for computing Local and global consistency algorithm by Zhou et al. |
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2 |
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3 References |
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4 ---------- |
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5 Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). |
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6 Learning with local and global consistency. |
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7 Advances in neural information processing systems, 16(16), 321-328. |
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8 """ |
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9 import networkx as nx |
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10 |
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11 from networkx.utils.decorators import not_implemented_for |
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12 from networkx.algorithms.node_classification.utils import ( |
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13 _get_label_info, |
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14 _init_label_matrix, |
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15 _propagate, |
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16 _predict, |
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17 ) |
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18 |
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19 __all__ = ["local_and_global_consistency"] |
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20 |
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21 |
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22 @not_implemented_for("directed") |
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23 def local_and_global_consistency(G, alpha=0.99, max_iter=30, label_name="label"): |
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24 """Node classification by Local and Global Consistency |
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25 |
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26 Parameters |
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27 ---------- |
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28 G : NetworkX Graph |
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29 alpha : float |
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30 Clamping factor |
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31 max_iter : int |
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32 Maximum number of iterations allowed |
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33 label_name : string |
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34 Name of target labels to predict |
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35 |
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36 Returns |
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37 ---------- |
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38 predicted : array, shape = [n_samples] |
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39 Array of predicted labels |
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40 |
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41 Raises |
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42 ------ |
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43 NetworkXError |
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44 If no nodes on `G` has `label_name`. |
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45 |
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46 Examples |
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47 -------- |
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48 >>> from networkx.algorithms import node_classification |
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49 >>> G = nx.path_graph(4) |
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50 >>> G.nodes[0]["label"] = "A" |
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51 >>> G.nodes[3]["label"] = "B" |
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52 >>> G.nodes(data=True) |
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53 NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}}) |
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54 >>> G.edges() |
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55 EdgeView([(0, 1), (1, 2), (2, 3)]) |
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56 >>> predicted = node_classification.local_and_global_consistency(G) |
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57 >>> predicted |
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58 ['A', 'A', 'B', 'B'] |
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59 |
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60 |
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61 References |
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62 ---------- |
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63 Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004). |
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64 Learning with local and global consistency. |
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65 Advances in neural information processing systems, 16(16), 321-328. |
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66 """ |
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67 try: |
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68 import numpy as np |
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69 except ImportError as e: |
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70 raise ImportError( |
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71 "local_and_global_consistency() requires numpy: ", "http://numpy.org/ " |
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72 ) from e |
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73 try: |
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74 from scipy import sparse |
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75 except ImportError as e: |
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76 raise ImportError( |
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77 "local_and_global_consistensy() requires scipy: ", "http://scipy.org/ " |
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78 ) from e |
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79 |
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80 def _build_propagation_matrix(X, labels, alpha): |
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81 """Build propagation matrix of Local and global consistency |
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82 |
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83 Parameters |
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84 ---------- |
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85 X : scipy sparse matrix, shape = [n_samples, n_samples] |
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86 Adjacency matrix |
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87 labels : array, shape = [n_samples, 2] |
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88 Array of pairs of node id and label id |
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89 alpha : float |
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90 Clamping factor |
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91 |
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92 Returns |
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93 ---------- |
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94 S : scipy sparse matrix, shape = [n_samples, n_samples] |
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95 Propagation matrix |
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96 |
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97 """ |
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98 degrees = X.sum(axis=0).A[0] |
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99 degrees[degrees == 0] = 1 # Avoid division by 0 |
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100 D2 = np.sqrt(sparse.diags((1.0 / degrees), offsets=0)) |
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101 S = alpha * D2.dot(X).dot(D2) |
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102 return S |
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103 |
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104 def _build_base_matrix(X, labels, alpha, n_classes): |
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105 """Build base matrix of Local and global consistency |
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106 |
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107 Parameters |
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108 ---------- |
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109 X : scipy sparse matrix, shape = [n_samples, n_samples] |
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110 Adjacency matrix |
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111 labels : array, shape = [n_samples, 2] |
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112 Array of pairs of node id and label id |
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113 alpha : float |
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114 Clamping factor |
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115 n_classes : integer |
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116 The number of classes (distinct labels) on the input graph |
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117 |
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118 Returns |
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119 ---------- |
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120 B : array, shape = [n_samples, n_classes] |
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121 Base matrix |
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122 """ |
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123 |
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124 n_samples = X.shape[0] |
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125 B = np.zeros((n_samples, n_classes)) |
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126 B[labels[:, 0], labels[:, 1]] = 1 - alpha |
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127 return B |
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128 |
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129 X = nx.to_scipy_sparse_matrix(G) # adjacency matrix |
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130 labels, label_dict = _get_label_info(G, label_name) |
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131 |
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132 if labels.shape[0] == 0: |
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133 raise nx.NetworkXError( |
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134 "No node on the input graph is labeled by '" + label_name + "'." |
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135 ) |
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136 |
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137 n_samples = X.shape[0] |
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138 n_classes = label_dict.shape[0] |
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139 F = _init_label_matrix(n_samples, n_classes) |
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140 |
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141 P = _build_propagation_matrix(X, labels, alpha) |
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142 B = _build_base_matrix(X, labels, alpha, n_classes) |
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143 |
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144 remaining_iter = max_iter |
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145 while remaining_iter > 0: |
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146 F = _propagate(P, F, B) |
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147 remaining_iter -= 1 |
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148 |
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149 predicted = _predict(F, label_dict) |
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150 |
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151 return predicted |