Mercurial > repos > miller-lab > genome_diversity
annotate assignment_of_optimal_breeding_pairs.py @ 39:e56023008e36 default tip
Changed revision of package_fisher_0_1_4 to be2fc454d121
Changed revision of package_matplotlib_1_2 to a03ee94316b5
author | miller-lab |
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date | Mon, 06 Jul 2015 10:32:24 -0400 |
parents | a631c2f6d913 |
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rev | line source |
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1 #!/usr/bin/env python2.6 |
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2 |
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3 import sys |
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4 import munkres |
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5 import random |
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6 |
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7 class Vertex(object): |
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8 def __init__(self, name): |
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9 self.name = name |
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10 self.neighbors = {} |
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11 self.color = 0 |
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12 self.explored = False |
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13 |
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14 def add_neighbor(self, neighbor, weight=0.0): |
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15 if neighbor in self.neighbors: |
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16 if self.neighbors[neighbor] != weight: |
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17 die('multiple edges not supported') |
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18 else: |
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19 self.neighbors[neighbor] = weight |
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20 |
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21 class Graph(object): |
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22 def __init__(self): |
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23 self.vertex_list = {} |
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24 self.vertices = 0 |
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25 self.max_weight = 0.0 |
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26 |
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27 def add_vertex(self, name): |
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28 if name not in self.vertex_list: |
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29 self.vertex_list[name] = Vertex(name) |
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30 self.vertices += 1 |
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31 return self.vertex_list[name] |
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32 |
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33 def add_edge(self, name1, name2, weight): |
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34 vertex1 = self.add_vertex(name1) |
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35 vertex2 = self.add_vertex(name2) |
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36 vertex1.add_neighbor(vertex2, weight) |
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37 vertex2.add_neighbor(vertex1, weight) |
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38 self.max_weight = max(self.max_weight, weight) |
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39 |
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40 def from_edge_file(self, filename): |
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41 fh = try_open(filename) |
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42 line_number = 0 |
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43 for line in fh: |
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44 line_number += 1 |
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45 line = line.rstrip('\r\n') |
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46 elems = line.split() |
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47 if len(elems) < 3: |
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48 die('too few columns on line {0} of {1}:\n{2}'.format(line_number, filename, line)) |
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49 name1 = elems[0] |
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50 name2 = elems[1] |
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51 weight = float_value(elems[2]) |
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52 if weight is None: |
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53 die('invalid weight on line {0} of {1}:\n{2}'.format(line_number, filename, line)) |
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54 self.add_edge(name1, name2, weight) |
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55 fh.close() |
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56 |
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57 def bipartite_partition(self): |
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58 vertices_left = self.vertex_list.values() |
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59 |
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60 while vertices_left: |
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61 fifo = [vertices_left[0]] |
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62 while fifo: |
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63 vertex = fifo.pop(0) |
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64 if not vertex.explored: |
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65 vertex.explored = True |
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66 vertices_left.remove(vertex) |
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67 |
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68 if vertex.color == 0: |
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69 vertex.color = 1 |
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70 neighbor_color = 2 |
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71 elif vertex.color == 1: |
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72 neighbor_color = 2 |
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73 elif vertex.color == 2: |
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74 neighbor_color = 1 |
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75 |
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76 for neighbor in vertex.neighbors: |
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77 if neighbor.color == 0: |
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78 neighbor.color = neighbor_color |
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79 elif neighbor.color != neighbor_color: |
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80 return None, None |
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81 fifo.append(neighbor) |
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82 |
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83 c1 = [] |
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84 c2 = [] |
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85 |
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86 for vertex in self.vertex_list.values(): |
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87 if vertex.color == 1: |
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88 c1.append(vertex) |
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89 elif vertex.color == 2: |
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90 c2.append(vertex) |
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91 |
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92 return c1, c2 |
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93 |
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94 def try_open(*args): |
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95 try: |
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96 return open(*args) |
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97 except IOError: |
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98 die('Failed opening file: {0}'.format(args[0])) |
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99 |
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100 def float_value(token): |
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101 try: |
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102 return float(token) |
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103 except ValueError: |
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104 return None |
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105 |
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106 def die(message): |
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107 print >> sys.stderr, message |
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108 sys.exit(1) |
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109 |
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110 def main(input, randomizations, output): |
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111 graph = Graph() |
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112 graph.from_edge_file(input) |
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113 c1, c2 = graph.bipartite_partition() |
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114 |
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115 if c1 is None: |
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116 die('Graph is not bipartite') |
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117 |
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118 if len(c1) + len(c2) != graph.vertices: |
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119 die('Bipartite partition failed: {0} + {1} != {2}'.format(len(c1), len(c2), graph.vertices)) |
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120 |
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121 with open(output, 'w') as ofh: |
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122 a1 = optimal_assignment(c1, c2, graph.max_weight) |
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123 optimal_total_weight = 0.0 |
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124 for a in a1: |
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125 optimal_total_weight += a[0].neighbors[a[1]] |
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126 |
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127 print >> ofh, 'optimal average {0:.3f}'.format(optimal_total_weight / len(a1)) |
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128 |
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129 if randomizations > 0: |
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130 random_total_count = 0 |
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131 random_total_weight = 0.0 |
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132 for i in range(randomizations): |
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133 a2 = random_assignment(c1, c2) |
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134 random_total_count += len(a2) |
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135 for a in a2: |
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136 random_total_weight += a[0].neighbors[a[1]] |
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137 print >> ofh, 'random average {0:.3f}'.format(random_total_weight / random_total_count) |
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138 |
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139 |
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140 for a in a1: |
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141 print >> ofh, '\t'.join([a[0].name, a[1].name]) |
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142 |
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143 def optimal_assignment(c1, c2, max_weight): |
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144 matrix = [] |
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145 assignment = [] |
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146 |
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147 for v1 in c1: |
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148 row = [] |
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149 for v2 in c2: |
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150 row.append(max_weight + 1.0 - v1.neighbors[v2]) |
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151 matrix.append(row) |
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152 |
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153 m = munkres.Munkres() |
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154 indexes = m.compute(matrix) |
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155 for row, column in indexes: |
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156 assignment.append([c1[row], c2[column]]) |
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157 |
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158 return assignment |
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159 |
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160 def random_assignment(c1, c2): |
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161 assignment = [] |
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162 |
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163 ## note, this assumes that graph is complete bipartite |
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164 ## this needs to be fixed |
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165 c1_len = len(c1) |
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166 c2_len = len(c2) |
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167 idx_list = list(range(max(c1_len, c2_len))) |
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168 random.shuffle(idx_list) |
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169 |
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170 if c1_len <= c2_len: |
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171 for i, v1 in enumerate(c1): |
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172 assignment.append([v1, c2[idx_list[i]]]) |
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173 else: |
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174 for i, v1 in enumerate(c2): |
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175 assignment.append([v1, c1[idx_list[i]]]) |
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176 |
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177 return assignment |
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178 |
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179 ################################################################################ |
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180 |
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181 if len(sys.argv) != 4: |
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182 die('Usage') |
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183 |
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184 input, randomizations, output = sys.argv[1:] |
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185 main(input, int(randomizations), output) |