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#!/usr/bin/env python
#-*- coding:utf-8 -*-
#---------------------------------------------------------------
# PyNLPl - Test Units for Search Algorithms
# by Maarten van Gompel, ILK, Universiteit van Tilburg
# http://ilk.uvt.nl/~mvgompel
# proycon AT anaproy DOT nl
#
# Licensed under GPLv3
#
#----------------------------------------------------------------
import sys
import os
import unittest
sys.path.append(sys.path[0] + '/../../')
os.environ['PYTHONPATH'] = sys.path[0] + '/../../'
from pynlpl.search import AbstractSearchState, DepthFirstSearch, BreadthFirstSearch, IterativeDeepening, HillClimbingSearch, BeamSearch
class ReorderSearchState(AbstractSearchState):
def __init__(self, tokens, parent = None):
self.tokens = tokens
super(ReorderSearchState, self).__init__(parent)
def expand(self):
#Operator: Swap two consecutive pairs
l = len(self.tokens)
for i in range(0,l - 1):
newtokens = self.tokens[:i]
newtokens.append(self.tokens[i + 1])
newtokens.append(self.tokens[i])
if i+2 < l:
newtokens += self.tokens[i+2:]
yield ReorderSearchState(newtokens, self)
def __hash__(self):
return hash(str(self))
def __eq__(self, other):
return str(self) == str(other)
def __str__(self):
return " ".join(self.tokens)
class InformedReorderSearchState(ReorderSearchState):
def __init__(self, tokens, goal = None, parent = None):
self.tokens = tokens
self.goal = goal
super(ReorderSearchState, self).__init__(parent)
def score(self):
"""Compute distortion"""
totaldistortion = 0
for i, token in enumerate(self.goal.tokens):
tokendistortion = 9999999
for j, token2 in enumerate(self.tokens):
if token == token2 and abs(i - j) < tokendistortion:
tokendistortion = abs(i - j)
totaldistortion += tokendistortion
return totaldistortion
def expand(self):
#Operator: Swap two consecutive pairs
l = len(self.tokens)
for i in range(0,l - 1):
newtokens = self.tokens[:i]
newtokens.append(self.tokens[i + 1])
newtokens.append(self.tokens[i])
if i+2 < l:
newtokens += self.tokens[i+2:]
yield InformedReorderSearchState(newtokens, self.goal, self)
inputstate = ReorderSearchState("a This test . sentence is".split(' '))
goalstate = ReorderSearchState("This is a test sentence .".split(' '))
class DepthFirstSearchTest(unittest.TestCase):
def test_solution(self):
"""Depth First Search"""
global inputstate, goalstate
search = DepthFirstSearch(inputstate ,graph=True, goal=goalstate)
solution = search.searchfirst()
#print "DFS:", search.traversalsize(), "nodes visited |",
self.assertEqual(solution, goalstate)
class BreadthFirstSearchTest(unittest.TestCase):
def test_solution(self):
"""Breadth First Search"""
global inputstate, goalstate
search = BreadthFirstSearch(inputstate ,graph=True, goal=goalstate)
solution = search.searchfirst()
#print "BFS:", search.traversalsize(), "nodes visited |",
self.assertEqual(solution, goalstate)
class IterativeDeepeningTest(unittest.TestCase):
def test_solution(self):
"""Iterative Deepening DFS"""
global inputstate, goalstate
search = IterativeDeepening(inputstate ,graph=True, goal=goalstate)
solution = search.searchfirst()
#print "It.Deep:", search.traversalsize(), "nodes visited |",
self.assertEqual(solution, goalstate)
informedinputstate = InformedReorderSearchState("a This test . sentence is".split(' '), goalstate)
#making a simple language model
class HillClimbingTest(unittest.TestCase):
def test_solution(self):
"""Hill Climbing"""
global informedinputstate
search = HillClimbingSearch(informedinputstate, graph=True, minimize=True,debug=False)
solution = search.searchbest()
self.assertTrue(solution) #TODO: this is not a test!
class BeamSearchTest(unittest.TestCase):
def test_minimizeC1(self):
"""Beam Search needle-in-haystack problem (beam=2, minimize)"""
#beamsize has been set to the minimum that yields the correct solution
global informedinputstate, solution, goalstate
search = BeamSearch(informedinputstate, beamsize=2, graph=True, minimize=True,debug=0, goal=goalstate)
solution = search.searchbest()
self.assertEqual( str(solution), str(goalstate) )
self.assertEqual( search.solutions, 1 )
def test_minimizeA1(self):
"""Beam Search optimisation problem A (beam=2, minimize)"""
#beamsize has been set to the minimum that yields the correct solution
global informedinputstate, solution, goalstate
search = BeamSearch(informedinputstate, beamsize=2, graph=True, minimize=True,debug=0)
solution = search.searchbest()
self.assertEqual( str(solution), str(goalstate) )
self.assertTrue( search.solutions > 1 ) #everything is a solution
def test_minimizeA2(self):
"""Beam Search optimisation problem A (beam=100, minimize)"""
#if a small beamsize works, a very large one should too
global informedinputstate, solution, goalstate
search = BeamSearch(informedinputstate, beamsize=100, graph=True, minimize=True,debug=0)
solution = search.searchbest()
self.assertEqual( str(solution), str(goalstate) )
self.assertTrue( search.solutions > 1 ) #everything is a solution
#def test_minimizeA3(self):
# """Beam Search optimisation problem A (eager mode, beam=2, minimize)"""
# #beamsize has been set to the minimum that yields the correct solution
# global informedinputstate, solution, goalstate
# search = BeamSearch(informedinputstate, beamsize=50, graph=True, minimize=True,eager=True,debug=2)
# solution = search.searchbest()
# self.assertEqual( str(solution), str(goalstate) )
def test_minimizeB1(self):
"""Beam Search optimisation problem (longer) (beam=3, minimize)"""
#beamsize has been set to the minimum that yields the correct solution
goalstate = InformedReorderSearchState("This is supposed to be a very long sentence .".split(' '))
informedinputstate = InformedReorderSearchState("a long very . sentence supposed to be This is".split(' '), goalstate)
search = BeamSearch(informedinputstate, beamsize=3, graph=True, minimize=True,debug=False)
solution = search.searchbest()
self.assertEqual(str(solution),str(goalstate))
if __name__ == '__main__':
unittest.main()
|
