Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/isomorphism/matchhelpers.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 """Functions which help end users define customize node_match and |
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2 edge_match functions to use during isomorphism checks. |
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3 """ |
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4 from itertools import permutations |
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5 import types |
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6 |
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7 __all__ = [ |
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8 "categorical_node_match", |
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9 "categorical_edge_match", |
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10 "categorical_multiedge_match", |
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11 "numerical_node_match", |
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12 "numerical_edge_match", |
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13 "numerical_multiedge_match", |
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14 "generic_node_match", |
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15 "generic_edge_match", |
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16 "generic_multiedge_match", |
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17 ] |
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18 |
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19 |
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20 def copyfunc(f, name=None): |
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21 """Returns a deepcopy of a function.""" |
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22 return types.FunctionType( |
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23 f.__code__, f.__globals__, name or f.__name__, f.__defaults__, f.__closure__ |
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24 ) |
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25 |
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26 |
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27 def allclose(x, y, rtol=1.0000000000000001e-05, atol=1e-08): |
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28 """Returns True if x and y are sufficiently close, elementwise. |
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29 |
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30 Parameters |
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31 ---------- |
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32 rtol : float |
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33 The relative error tolerance. |
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34 atol : float |
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35 The absolute error tolerance. |
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36 |
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37 """ |
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38 # assume finite weights, see numpy.allclose() for reference |
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39 for xi, yi in zip(x, y): |
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40 if not (abs(xi - yi) <= atol + rtol * abs(yi)): |
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41 return False |
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42 return True |
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43 |
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44 |
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45 def close(x, y, rtol=1.0000000000000001e-05, atol=1e-08): |
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46 """Returns True if x and y are sufficiently close. |
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47 |
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48 Parameters |
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49 ---------- |
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50 rtol : float |
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51 The relative error tolerance. |
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52 atol : float |
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53 The absolute error tolerance. |
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54 |
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55 """ |
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56 # assume finite weights, see numpy.allclose() for reference |
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57 return abs(x - y) <= atol + rtol * abs(y) |
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58 |
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59 |
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60 categorical_doc = """ |
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61 Returns a comparison function for a categorical node attribute. |
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62 |
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63 The value(s) of the attr(s) must be hashable and comparable via the == |
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64 operator since they are placed into a set([]) object. If the sets from |
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65 G1 and G2 are the same, then the constructed function returns True. |
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66 |
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67 Parameters |
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68 ---------- |
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69 attr : string | list |
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70 The categorical node attribute to compare, or a list of categorical |
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71 node attributes to compare. |
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72 default : value | list |
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73 The default value for the categorical node attribute, or a list of |
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74 default values for the categorical node attributes. |
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75 |
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76 Returns |
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77 ------- |
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78 match : function |
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79 The customized, categorical `node_match` function. |
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80 |
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81 Examples |
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82 -------- |
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83 >>> import networkx.algorithms.isomorphism as iso |
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84 >>> nm = iso.categorical_node_match("size", 1) |
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85 >>> nm = iso.categorical_node_match(["color", "size"], ["red", 2]) |
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86 |
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87 """ |
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88 |
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89 |
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90 def categorical_node_match(attr, default): |
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91 if isinstance(attr, str): |
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92 |
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93 def match(data1, data2): |
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94 return data1.get(attr, default) == data2.get(attr, default) |
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95 |
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96 else: |
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97 attrs = list(zip(attr, default)) # Python 3 |
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98 |
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99 def match(data1, data2): |
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100 return all(data1.get(attr, d) == data2.get(attr, d) for attr, d in attrs) |
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101 |
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102 return match |
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103 |
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104 |
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105 try: |
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106 categorical_edge_match = copyfunc(categorical_node_match, "categorical_edge_match") |
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107 except NotImplementedError: |
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108 # IronPython lacks support for types.FunctionType. |
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109 # https://github.com/networkx/networkx/issues/949 |
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110 # https://github.com/networkx/networkx/issues/1127 |
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111 def categorical_edge_match(*args, **kwargs): |
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112 return categorical_node_match(*args, **kwargs) |
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113 |
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114 |
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115 def categorical_multiedge_match(attr, default): |
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116 if isinstance(attr, str): |
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117 |
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118 def match(datasets1, datasets2): |
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119 values1 = {data.get(attr, default) for data in datasets1.values()} |
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120 values2 = {data.get(attr, default) for data in datasets2.values()} |
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121 return values1 == values2 |
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122 |
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123 else: |
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124 attrs = list(zip(attr, default)) # Python 3 |
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125 |
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126 def match(datasets1, datasets2): |
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127 values1 = set() |
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128 for data1 in datasets1.values(): |
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129 x = tuple(data1.get(attr, d) for attr, d in attrs) |
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130 values1.add(x) |
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131 values2 = set() |
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132 for data2 in datasets2.values(): |
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133 x = tuple(data2.get(attr, d) for attr, d in attrs) |
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134 values2.add(x) |
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135 return values1 == values2 |
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136 |
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137 return match |
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138 |
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139 |
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140 # Docstrings for categorical functions. |
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141 categorical_node_match.__doc__ = categorical_doc |
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142 categorical_edge_match.__doc__ = categorical_doc.replace("node", "edge") |
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143 tmpdoc = categorical_doc.replace("node", "edge") |
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144 tmpdoc = tmpdoc.replace("categorical_edge_match", "categorical_multiedge_match") |
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145 categorical_multiedge_match.__doc__ = tmpdoc |
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146 |
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147 |
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148 numerical_doc = """ |
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149 Returns a comparison function for a numerical node attribute. |
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150 |
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151 The value(s) of the attr(s) must be numerical and sortable. If the |
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152 sorted list of values from G1 and G2 are the same within some |
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153 tolerance, then the constructed function returns True. |
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154 |
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155 Parameters |
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156 ---------- |
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157 attr : string | list |
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158 The numerical node attribute to compare, or a list of numerical |
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159 node attributes to compare. |
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160 default : value | list |
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161 The default value for the numerical node attribute, or a list of |
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162 default values for the numerical node attributes. |
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163 rtol : float |
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164 The relative error tolerance. |
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165 atol : float |
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166 The absolute error tolerance. |
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167 |
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168 Returns |
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169 ------- |
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170 match : function |
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171 The customized, numerical `node_match` function. |
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172 |
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173 Examples |
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174 -------- |
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175 >>> import networkx.algorithms.isomorphism as iso |
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176 >>> nm = iso.numerical_node_match("weight", 1.0) |
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177 >>> nm = iso.numerical_node_match(["weight", "linewidth"], [0.25, 0.5]) |
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178 |
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179 """ |
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180 |
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181 |
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182 def numerical_node_match(attr, default, rtol=1.0000000000000001e-05, atol=1e-08): |
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183 if isinstance(attr, str): |
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184 |
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185 def match(data1, data2): |
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186 return close( |
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187 data1.get(attr, default), data2.get(attr, default), rtol=rtol, atol=atol |
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188 ) |
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189 |
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190 else: |
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191 attrs = list(zip(attr, default)) # Python 3 |
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192 |
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193 def match(data1, data2): |
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194 values1 = [data1.get(attr, d) for attr, d in attrs] |
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195 values2 = [data2.get(attr, d) for attr, d in attrs] |
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196 return allclose(values1, values2, rtol=rtol, atol=atol) |
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197 |
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198 return match |
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199 |
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200 |
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201 try: |
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202 numerical_edge_match = copyfunc(numerical_node_match, "numerical_edge_match") |
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203 except NotImplementedError: |
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204 # IronPython lacks support for types.FunctionType. |
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205 # https://github.com/networkx/networkx/issues/949 |
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206 # https://github.com/networkx/networkx/issues/1127 |
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207 def numerical_edge_match(*args, **kwargs): |
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208 return numerical_node_match(*args, **kwargs) |
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209 |
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210 |
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211 def numerical_multiedge_match(attr, default, rtol=1.0000000000000001e-05, atol=1e-08): |
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212 if isinstance(attr, str): |
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213 |
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214 def match(datasets1, datasets2): |
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215 values1 = sorted([data.get(attr, default) for data in datasets1.values()]) |
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216 values2 = sorted([data.get(attr, default) for data in datasets2.values()]) |
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217 return allclose(values1, values2, rtol=rtol, atol=atol) |
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218 |
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219 else: |
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220 attrs = list(zip(attr, default)) # Python 3 |
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221 |
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222 def match(datasets1, datasets2): |
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223 values1 = [] |
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224 for data1 in datasets1.values(): |
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225 x = tuple(data1.get(attr, d) for attr, d in attrs) |
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226 values1.append(x) |
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227 values2 = [] |
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228 for data2 in datasets2.values(): |
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229 x = tuple(data2.get(attr, d) for attr, d in attrs) |
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230 values2.append(x) |
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231 values1.sort() |
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232 values2.sort() |
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233 for xi, yi in zip(values1, values2): |
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234 if not allclose(xi, yi, rtol=rtol, atol=atol): |
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235 return False |
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236 else: |
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237 return True |
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238 |
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239 return match |
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240 |
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241 |
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242 # Docstrings for numerical functions. |
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243 numerical_node_match.__doc__ = numerical_doc |
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244 numerical_edge_match.__doc__ = numerical_doc.replace("node", "edge") |
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245 tmpdoc = numerical_doc.replace("node", "edge") |
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246 tmpdoc = tmpdoc.replace("numerical_edge_match", "numerical_multiedge_match") |
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247 numerical_multiedge_match.__doc__ = tmpdoc |
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248 |
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249 |
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250 generic_doc = """ |
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251 Returns a comparison function for a generic attribute. |
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252 |
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253 The value(s) of the attr(s) are compared using the specified |
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254 operators. If all the attributes are equal, then the constructed |
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255 function returns True. |
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256 |
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257 Parameters |
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258 ---------- |
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259 attr : string | list |
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260 The node attribute to compare, or a list of node attributes |
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261 to compare. |
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262 default : value | list |
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263 The default value for the node attribute, or a list of |
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264 default values for the node attributes. |
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265 op : callable | list |
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266 The operator to use when comparing attribute values, or a list |
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267 of operators to use when comparing values for each attribute. |
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268 |
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269 Returns |
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270 ------- |
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271 match : function |
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272 The customized, generic `node_match` function. |
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273 |
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274 Examples |
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275 -------- |
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276 >>> from operator import eq |
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277 >>> from networkx.algorithms.isomorphism.matchhelpers import close |
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278 >>> from networkx.algorithms.isomorphism import generic_node_match |
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279 >>> nm = generic_node_match("weight", 1.0, close) |
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280 >>> nm = generic_node_match("color", "red", eq) |
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281 >>> nm = generic_node_match(["weight", "color"], [1.0, "red"], [close, eq]) |
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282 |
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283 """ |
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284 |
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285 |
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286 def generic_node_match(attr, default, op): |
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287 if isinstance(attr, str): |
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288 |
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289 def match(data1, data2): |
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290 return op(data1.get(attr, default), data2.get(attr, default)) |
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291 |
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292 else: |
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293 attrs = list(zip(attr, default, op)) # Python 3 |
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294 |
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295 def match(data1, data2): |
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296 for attr, d, operator in attrs: |
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297 if not operator(data1.get(attr, d), data2.get(attr, d)): |
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298 return False |
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299 else: |
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300 return True |
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301 |
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302 return match |
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303 |
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304 |
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305 try: |
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306 generic_edge_match = copyfunc(generic_node_match, "generic_edge_match") |
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307 except NotImplementedError: |
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308 # IronPython lacks support for types.FunctionType. |
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309 # https://github.com/networkx/networkx/issues/949 |
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310 # https://github.com/networkx/networkx/issues/1127 |
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311 def generic_edge_match(*args, **kwargs): |
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312 return generic_node_match(*args, **kwargs) |
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313 |
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314 |
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315 def generic_multiedge_match(attr, default, op): |
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316 """Returns a comparison function for a generic attribute. |
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317 |
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318 The value(s) of the attr(s) are compared using the specified |
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319 operators. If all the attributes are equal, then the constructed |
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320 function returns True. Potentially, the constructed edge_match |
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321 function can be slow since it must verify that no isomorphism |
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322 exists between the multiedges before it returns False. |
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323 |
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324 Parameters |
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325 ---------- |
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326 attr : string | list |
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327 The edge attribute to compare, or a list of node attributes |
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328 to compare. |
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329 default : value | list |
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330 The default value for the edge attribute, or a list of |
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331 default values for the dgeattributes. |
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332 op : callable | list |
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333 The operator to use when comparing attribute values, or a list |
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334 of operators to use when comparing values for each attribute. |
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335 |
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336 Returns |
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337 ------- |
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338 match : function |
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339 The customized, generic `edge_match` function. |
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340 |
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341 Examples |
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342 -------- |
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343 >>> from operator import eq |
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344 >>> from networkx.algorithms.isomorphism.matchhelpers import close |
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345 >>> from networkx.algorithms.isomorphism import generic_node_match |
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346 >>> nm = generic_node_match("weight", 1.0, close) |
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347 >>> nm = generic_node_match("color", "red", eq) |
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348 >>> nm = generic_node_match(["weight", "color"], [1.0, "red"], [close, eq]) |
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349 ... |
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350 |
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351 """ |
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352 |
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353 # This is slow, but generic. |
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354 # We must test every possible isomorphism between the edges. |
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355 if isinstance(attr, str): |
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356 attr = [attr] |
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357 default = [default] |
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358 op = [op] |
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359 attrs = list(zip(attr, default)) # Python 3 |
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360 |
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361 def match(datasets1, datasets2): |
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362 values1 = [] |
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363 for data1 in datasets1.values(): |
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364 x = tuple(data1.get(attr, d) for attr, d in attrs) |
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365 values1.append(x) |
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366 values2 = [] |
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367 for data2 in datasets2.values(): |
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368 x = tuple(data2.get(attr, d) for attr, d in attrs) |
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369 values2.append(x) |
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370 for vals2 in permutations(values2): |
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371 for xi, yi in zip(values1, vals2): |
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372 if not all(map(lambda x, y, z: z(x, y), xi, yi, op)): |
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373 # This is not an isomorphism, go to next permutation. |
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374 break |
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375 else: |
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376 # Then we found an isomorphism. |
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377 return True |
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378 else: |
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379 # Then there are no isomorphisms between the multiedges. |
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380 return False |
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381 |
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382 return match |
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383 |
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384 |
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385 # Docstrings for numerical functions. |
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386 generic_node_match.__doc__ = generic_doc |
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387 generic_edge_match.__doc__ = generic_doc.replace("node", "edge") |