Section 20.6. Graph Searching

20.6. Graph Searching

Many problems can be represented as a graph, which is a set of states with transitions ("arcs") that lead from one state to another. For example, planning a route for a trip is really a graph search problem in disguisethe states are places you'd like to visit, and the arcs are the transportation links between them.

This section presents simple Python programs that search through a directed, cyclic graph to find the paths between a start state and a goal. Graphs can be more general than trees because links may point at arbitrary nodeseven ones already searched (hence the word cyclic).

The graph used to test searchers in this section is sketched in Figure 20-1. Arrows at the end of arcs indicate valid paths (e.g., A leads to B, E, and G). The search algorithms will traverse this graph in a depth-first fashion, and they will trap cycles in order to avoid looping. If you pretend that this is a map, where nodes represent cities and arcs represent roads, this example will probably seem a bit more meaningful.

Figure 20-1. A directed graph

The first thing we need to do is choose a way to represent this graph in a Python script. One approach is to use built-in datatypes and searcher functions. The file in Example 20-15 builds the test graph as a simple dictionary: each state is a dictionary key, with a list of keys of nodes it leads to (i.e., its arcs). This file also defines a function that we'll use to run a few searches in the graph.

Example 20-15. PP3E\Dstruct\Classics\

 Graph = {'A':  ['B', 'E', 'G'],          'B':  ['C'],                        # a directed, cyclic graph          'C':  ['D', 'E'],                   # stored as a dictionary          'D':  ['F'],                        # 'key' leads-to [nodes]          'E':  ['C', 'F', 'G'],          'F':  [ ],          'G':  ['A']  } def tests(searcher):                         # test searcher function     print searcher('E', 'D', Graph)          # find all paths from 'E' to 'D'     for x in ['AG', 'GF', 'BA', 'DA']:         print x, searcher(x[0], x[1], Graph) 

Now, let's code two modules that implement the actual search algorithms. They are both independent of the graph to be searched (it is passed in as an argument). The first searcher, in Example 20-16, uses recursion to walk through the graph.

Example 20-16. PP3E\Dstruct\Classics\

 # find all paths from start to goal in graph def search(start, goal, graph):     solns = []     generate([start], goal, solns, graph)              # collect paths     solns.sort( lambda x, y: cmp(len(x), len(y)) )     # sort by path length     return solns def generate(path, goal, solns, graph):     state = path[-1]     if state == goal:                                  # found goal here         solns.append(path)                             # change solns in-place     else:                                              # check all arcs here         for arc in graph[state]:                       # skip cycles on path             if arc not in path:                 generate(path + [arc], goal, solns, graph) if _ _name_ _ == '_ _main_ _':     import gtestfunc     gtestfunc.tests(search) 

The second searcher, in Example 20-17, uses an explicit stack of paths to be expanded using the tuple-tree stack representation we explored earlier in this chapter.

Example 20-17. PP3E\Dstruct\Classics\

 # use paths stack instead of recursion def search(start, goal, graph):     solns = generate(([start], []), goal, graph)     solns.sort( lambda x, y: cmp(len(x), len(y)) )     return solns def generate(paths, goal, graph):                      # returns solns list     solns = []                                         # use a tuple-stack     while paths:         front, paths = paths                           # pop the top path         state = front[-1]         if state == goal:             solns.append(front)                        # goal on this path         else:             for arc in graph[state]:                   # add all extensions                 if arc not in front:                     paths = (front + [arc]), paths     return solns if _ _name_ _ == '_ _main_ _':     import gtestfunc     gtestfunc.tests(search) 

To avoid cycles, both searchers keep track of nodes visited along a path. If an extension is already on the current path, it is a loop. The resulting solutions list is sorted by increasing lengths using the list sort method and the built-in cmp comparison function. To test the searcher modules, simply run them; their self-test code calls the canned search test in the gtestfunc module:

 C:\...\PP3E\Dstruct\Classics>python [['E', 'C', 'D'], ['E', 'G', 'A', 'B', 'C', 'D']] AG [['A', 'G'], ['A', 'E', 'G'], ['A', 'B', 'C', 'E', 'G']] GF [['G', 'A', 'E', 'F'], ['G', 'A', 'B', 'C', 'D', 'F'],     ['G', 'A', 'B', 'C', 'E', 'F'], ['G', 'A', 'E', 'C', 'D', 'F']] BA [['B', 'C', 'E', 'G', 'A']] DA [] C:\...\PP3E\Dstruct\Classics>python [['E', 'C', 'D'], ['E', 'G', 'A', 'B', 'C', 'D']] AG [['A', 'G'], ['A', 'E', 'G'], ['A', 'B', 'C', 'E', 'G']] GF [['G', 'A', 'E', 'F'], ['G', 'A', 'E', 'C', 'D', 'F'],     ['G', 'A', 'B', 'C', 'E', 'F'], ['G', 'A', 'B', 'C', 'D', 'F']] BA [['B', 'C', 'E', 'G', 'A']] DA [] 

This output shows lists of possible paths through the test graph; I added two line breaks to make it more readable. Notice that both searchers find the same paths in all tests, but the order in which they find those solutions may differ. The gsearch2 order depends on how and when extensions are added to its path's stack.

20.6.1. Moving Graphs to Classes

Using dictionaries to represent graphs is efficient: connected nodes are located by a fast hashing operation. But depending on the application, other representations might make more sense. For instance, classes can be used to model nodes in a network too, much like the binary tree example earlier. As classes, nodes may contain content useful for more sophisticated searches. To illustrate, Example 20-18 shows an alternative coding for our graph searcher; its algorithm is closest to gsearch1.

Example 20-18. PP3E\Dstruct\Classics\

 # build graph with objects that know how to search class Graph:     def _ _init_ _(self, label, extra=None): = label                                # nodes=inst objects = extra                                # graph=linked objs         self.arcs = []     def _ _repr_ _(self):         return     def search(self, goal):         Graph.solns = []         self.generate([self], goal)         Graph.solns.sort(lambda x,y: cmp(len(x), len(y)))         return Graph.solns     def generate(self, path, goal):         if self == goal:                                 # class == tests addr             Graph.solns.append(path)                     # or self.solns: same         else:             for arc in self.arcs:                 if arc not in path:                     arc.generate(path + [arc], goal) if _ _name_ _ == '_ _main_ _':     import gtestobj1     gtestobj1.tests(Graph) 

In this version, graphs are represented as a network of embedded class instance objects. Each node in the graph contains a list of the node objects it leads to (arcs), which it knows how to search. The generate method walks through the objects in the graph. But this time, links are directly available on each node's arcs list; there is no need to index (or pass) a dictionary to find linked objects.

To test, the module in Example 20-19 builds the test graph again, this time using linked instances of the Graph class. Notice the use of exec in the self-test code: it executes dynamically constructed strings to do the work of seven assignment statements (A=Graph('A'), B=Graph('B'), and so on).

Example 20-19. PP3E\Dstruct\Classics\

 def tests(Graph):     for name in "ABCDEFG":                               # make objects first         exec "%s = Graph('%s')" % (name, name)           # label=variable-name     A.arcs = [B, E, G]     B.arcs = [C]                 # now configure their links:     C.arcs = [D, E]              # embedded class-instance list     D.arcs = [F]     E.arcs = [C, F, G]     G.arcs = [A]     for (start, stop) in [(E,D), (A,G), (G,F), (B,A), (D,A)]:         print 

Run this test by running the graph module to pass in a graph class, like this:

 C:\...\PP3E\Dstruct\Classics>python [[E, C, D], [E, G, A, B, C, D]] [[A, G], [A, E, G], [A, B, C, E, G]] [[G, A, E, F], [G, A, B, C, D, F], [G, A, B, C, E, F], [G, A, E, C, D, F]] [[B, C, E, G, A]] [] 

The results are the same as for the functions, but node name labels are not quoted: nodes on path lists here are Graph instances, and this class's _ _repr_ _ scheme suppresses quotes. Example 20-20 is one last graph test before we move on; sketch the nodes and arcs on paper if you have more trouble following the paths than Python.

Example 20-20. PP3E\Dstruct\Classics\

 from graph import Graph S = Graph('s') P = Graph('p')       # a graph of spam A = Graph('a')       # make node objects M = Graph('m') S.arcs = [P, M]      # S leads to P and M P.arcs = [S, M, A]   # arcs: embedded objects A.arcs = [M] print    # find all paths from S to M 

This test finds three paths in its graph between nodes S and M. If you'd like to see more Python graph code, check out the files in the directory MoreGraphs in this book's examples distribution. These are roughly the same as the ones listed here, but they add user interaction as each solution is found. In addition, we've really only scratched the surface of this domain here; see other books for additional topics (e.g., breadth- and best-first search):

 C:\...\PP3E\Dstruct\Classics>python [[s, m], [s, p, m], [s, p, a, m]] 

Programming Python
Programming Python
ISBN: 0596009259
EAN: 2147483647
Year: 2004
Pages: 270
Authors: Mark Lutz

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