def testDisappearWhenCreatingChildren(self):
testConfig = ap.MyWorldConfig('testConfig.xml');
myWorld = TestMyWorld(testConfig)
myWorld.initialize()
myWorld.run()
index = Point2DInt(0,0)
for index._x in range(testConfig.size._width):
for index._y in range(testConfig.size._height):
myWorld.setValue('ground', index, ap.cellTypes.eWater)
myWorld.setValue('ground', Point2DInt(0,0), ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,0), ap.cellTypes.eInterdune)
myAP = ap.AP('ap_0')
myWorld.addAgent(myAP)
myAP._population[0] =-1
myAP._population[1] =-1
myAP._population.append(20)
myAP2 = ap.AP('ap_1')
myWorld.addAgent(myAP2)
myAP2._population[0] =-1
myAP2._population[1] =-1
myAP2._population.append(20)
myAP.createAgent(2)
self.assertEqual(myAP.getNumberOfIndividuals(),0)
self.assertEqual(myAP2.getNumberOfIndividuals(),0)
def main():
if len(sys.argv)==1:
print("Syntax: analyse.py <testbed>")
exit(0)
testbed_name = sys.argv[1]
queries = readin_testbed_queries(testbed_name)
stats = {}
stats['unmodified'] = {'map': 0, 'avg_ndcg' : 0}
stats['modified'] = {'map': 0, 'avg_ndcg' : 0}
# iterate through queries applying modified and unmodified search to obtain AP and NDCG values
for query_file_name in queries:
query_sentence = queries[query_file_name]
temp_arr = query_file_name.split('.')
query_number = temp_arr[1]
print()
print('='*100)
print('Carrying out analysis for query : ' + query_sentence)
# analysis using unmodified engine
result, accum, titles = submit_query(testbed_name, query_sentence, False)
ap_score = ap.AP(result, query_number, testbed_name, parameters.docs_to_consider)
ndcg_score = ndcg.NDCG(result, query_number, testbed_name, parameters.docs_to_consider)
stats['unmodified']['map'] += ap_score
stats['unmodified']['avg_ndcg'] += ndcg_score
print('*'*100)
print('Unmodified engine AP score : ' + str(ap_score))
print('Unmodified engine NDCG score : ' + str(ndcg_score))
# analysis using modified engine
result, accum, titles = submit_query(testbed_name, query_sentence, True)
ap_score =ap.AP(result, query_number, testbed_name, parameters.docs_to_consider)
ndcg_score = ndcg.NDCG(result, query_number, testbed_name, parameters.docs_to_consider)
stats['modified']['map'] += ap_score
stats['modified']['avg_ndcg'] += ndcg_score
print('*'*100)
print('Modified engine AP score : ' + str(ap_score))
print('Modified engine NDCG score : ' + str(ndcg_score))
# calculate MAP and average NDCG
num_queries = len(queries)
stats['unmodified']['map'] = stats['unmodified']['map']/num_queries
stats['unmodified']['avg_ndcg'] = stats['unmodified']['avg_ndcg']/num_queries
stats['modified']['map'] = stats['modified']['map']/num_queries
stats['modified']['avg_ndcg'] = stats['modified']['avg_ndcg']/num_queries
print()
print("+"*100)
print('Unmodified engine MAP : ' + str(stats['unmodified']['map']))
print('Unmodified engine average NDCG : ' + str(stats['unmodified']['avg_ndcg']))
print("*"*100)
print('Modified engine MAP : ' + str(stats['modified']['map']))
print('Modified engine average NDCG : ' + str(stats['modified']['avg_ndcg']))
def testSearchSuitableHome(self):
testConfig = ap.MyWorldConfig('testConfig.xml');
myWorld = TestMyWorld(testConfig)
myWorld.initialize()
myWorld.run()
index = Point2DInt(0,0)
for index._x in range(testConfig.size._width):
for index._y in range(testConfig.size._height):
myWorld.setValue('ground', index, ap.cellTypes.eWater)
myWorld.setValue('ground', Point2DInt(0,0), ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,0), ap.cellTypes.eInterdune)
myAP = ap.AP('ap_0')
myWorld.addAgent(myAP)
myWorld._yearRainfall = 444.0
myWorld.updateCalories()
myAP.searchSuitableHome()
self.assertEqual(myAP.position._x,0)
self.assertEqual(myAP.position._y,0)
self.assertEqual(len(myAP._plots), 1)
def testManageAnimals(self):
myAP = ap.AP('ap_1')
myAP._animalsProbability = 1.0
self.assertTrue(myAP._numAnimals== 0)
testConfig = ap.MyWorldConfig('testConfig.xml');
myWorld = TestMyWorld(testConfig)
myWorld.initialize()
myWorld.run()
index = Point2DInt(0,0)
for index._x in range(testConfig.size._width):
for index._y in range(testConfig.size._height):
myWorld.setValue('ground', index, ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,0), ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,0), ap.cellTypes.eInterdune)
myWorld.addAgent(myAP)
myAP.position = myWorld.getRandomDune()
myAP.manageAnimals()
myAP.manageAnimals()
myAP.manageAnimals()
self.assertTrue(myAP._numAnimals== 1)
def testAbandonPlots(self):
testConfig = ap.MyWorldConfig('testConfig.xml');
myWorld = TestMyWorld(testConfig)
myWorld.initialize()
myWorld.run()
index = Point2DInt(0,0)
for index._x in range(testConfig.size._width):
for index._y in range(testConfig.size._height):
myWorld.setValue('ground', index, ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,0), ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,0), ap.cellTypes.eInterdune)
myAP = ap.AP('ap_0')
myWorld.addAgent(myAP)
myAP.position = myWorld.getRandomDune()
myAP._population.append(2)
myAP._population.append(5)
myAP._population.append(10)
myAP._population.append(13)
myWorld.stepEnvironment()
myAP._population.pop()
myAP._population.pop()
myAP._population.pop()
myAP._population.pop()
myAP.searchSuitableHome()
self.assertEqual(len(myAP._plots), 1)
def testNumberOfPlotsNeeded(self):
testConfig = ap.MyWorldConfig('testConfig.xml');
myWorld = TestMyWorld(testConfig)
myWorld.initialize()
myWorld.run()
index = Point2DInt(0,0)
for index._x in range(testConfig.size._width):
for index._y in range(testConfig.size._height):
myWorld.setValue('ground', index, ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,0), ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,0), ap.cellTypes.eInterdune)
myAP = ap.AP('ap_0')
myWorld.addAgent(myAP)
myAP.position = myWorld.getRandomDune()
myAP._population.append(2)
myAP._population.append(5)
myAP._population.append(10)
myAP._population.append(13)
myWorld._yearRainfall = 444.0
myWorld.updateCalories()
myWorld.increaseYearsUsed()
self.assertEqual(myAP.plotsNeeded(), 2)
myAP.searchSuitableHome()
self.assertEqual(len(myAP._plots), 2)
def testSurvival(self):
testConfig = ap.MyWorldConfig('testConfig.xml');
myWorld = TestMyWorld(testConfig)
myWorld.initialize()
myWorld.run()
index = Point2DInt(0,0)
for index._x in range(testConfig.size._width):
for index._y in range(testConfig.size._height):
myWorld.setValue('ground', index, ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,0), ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,0), ap.cellTypes.eInterdune)
myAP = ap.AP('ap_0')
myWorld.addAgent(myAP)
myAP.position = myWorld.getRandomDune()
myWorld._yearRainfall = 444
myAP.searchSuitableHome()
myWorld.updateCalories()
myAP.updateState()
self.assertEqual(myAP._starvationRate, 0)
def testSurplus(self):
testConfig = ap.MyWorldConfig('testConfig.xml');
myWorld = TestMyWorld(testConfig)
myWorld.initialize()
myWorld.run()
index = Point2DInt(0,0)
for index._x in range(testConfig.size._width):
for index._y in range(testConfig.size._height):
myWorld.setValue('ground', index, ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,0), ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,0), ap.cellTypes.eInterdune)
myAP = ap.AP('ap_0')
myWorld.addAgent(myAP)
myAP.position = myWorld.getRandomDune()
myWorld._yearRainfall = 444.0
myWorld.updateCalories()
myAP.searchSuitableHome()
myAP.manageFarmActivities()
self.assertTrue(myAP._surplus> 0)
myAP = ap.AP('ap_1')
myWorld.addAgent(myAP)
myAP.position = myWorld.getRandomDune()
myWorld._yearRainfall = 50
myWorld.updateCalories()
myAP.searchSuitableHome()
myAP.manageFarmActivities()
self.assertTrue(myAP._surplus== 0)
myAP = ap.AP('ap_2')
myWorld.addAgent(myAP)
myAP.position = myWorld.getRandomDune()
myWorld._yearRainfall = 1100.0
myWorld.updateCalories()
myAP.searchSuitableHome()
myAP.manageFarmActivities()
self.assertTrue(myAP._surplus== 0)
def testCaloriesPerAge(self):
testConfig = ap.MyWorldConfig('testConfig.xml');
myWorld = TestMyWorld(testConfig)
myWorld.initialize()
myAP = ap.AP('ap_0')
myAP._population.append(2)
myAP._population.append(5)
myAP._population.append(10)
myAP._population.append(13)
myWorld.addAgent(myAP)
self.assertTrue(abs(myAP.getNeededCalories()-9175.9*365.0) < 50.0)
myWorld.run()
def testSurvivalNewPlot(self):
testConfig = ap.MyWorldConfig('testConfig.xml');
myWorld = TestMyWorld(testConfig)
myWorld.initialize()
index = Point2DInt(0,0)
for index._x in range(testConfig.size._width):
for index._y in range(testConfig.size._height):
myWorld.setValue('ground', index, ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,0), ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(0,1), ap.cellTypes.eDune)
myWorld.setValue('ground', Point2DInt(1,1), ap.cellTypes.eInterdune)
myWorld.setValue('ground', Point2DInt(1,0), ap.cellTypes.eInterdune)
myAP = ap.AP('ap_0')
myWorld.addAgent(myAP)
myAP.position = myWorld.getRandomDune()
myWorld._yearRainfall = 444.0
myWorld.updateCalories()
myWorld.increaseYearsUsed()
myAP.searchSuitableHome()
myAP.manageFarmActivities()
plot = Point2DInt(myAP._plots[0]._x, myAP._plots[0]._y)
self.assertEqual(plot._y, myAP._plots[0]._y)
self.assertTrue(myAP._starvationRate < 1)
myWorld.updateCalories()
myWorld.increaseYearsUsed()
myAP.manageFarmActivities()
myWorld.updateCalories()
myWorld.increaseYearsUsed()
myAP.manageFarmActivities()
self.assertNotEqual(plot._y, myAP._plots[0]._y)
self.assertNotEqual(-1, myAP._plots[0]._y)
self.assertTrue(myAP._starvationRate < 1)
myWorld.updateCalories()
myWorld.increaseYearsUsed()
myAP.updateState()
self.assertNotEqual(plot._y, myAP._plots[0]._y)
self.assertNotEqual(-1, myAP._plots[0]._y)
self.assertTrue(myAP._starvationRate < 1)
def testDisappearNotPlot(self):
testConfig = ap.MyWorldConfig('testConfig.xml');
myWorld = TestMyWorld(testConfig)
myWorld.initialize()
myAP = ap.AP('ap_0')
myWorld.addAgent(myAP)
index = Point2DInt(0,0)
for index._x in range(testConfig.size._width):
for index._y in range(testConfig.size._height):
myWorld.setValue('ground', index, ap.cellTypes.eDune)
myAP.position = myWorld.getRandomDune()
myWorld.run()
reference = myWorld.getAgent('ap_0')
self.assertEqual(reference, None)
def get_testbed_stats(testbed_name, queries):
MAP = 0
avg_NDCG = 0
# get AP and NDCG score for each query
for query_file_name in queries:
query = queries[query_file_name]
result, accum, titles = analyse.submit_query(testbed_name, query, True)
temp_arr = query_file_name.split('.')
query_number = temp_arr[1]
ap_score = ap.AP(result, query_number, testbed_name,
parameters.docs_to_consider)
ndcg_score = ndcg.NDCG(result, query_number, testbed_name,
parameters.docs_to_consider)
MAP += ap_score
avg_NDCG += ndcg_score
MAP = MAP / len(queries)
avg_NDCG = avg_NDCG / len(queries)
return MAP, avg_NDCG
def get_network(self):
for item in self.data:
if 'latitude' in item:
lat = float(item['latitude'])
else:
continue
if 'longitude' in item:
lon = float(item['longitude'])
else:
continue
if 'altitude' in item:
alt = float(item['altitude'])
else:
alt = 10.0
if 'radius' in item:
rad = int(item['radius'], 10)
else:
rad = 100
self.ap_list.append(ap.AP(lat, lon, alt, rad))
self.ap_list[-1].get_cover()
self.net = net.Network(self.ap_list)
def main():
if len(sys.argv) == 1:
print(
"Syntax: optimise.py <option -a for all or -s for single testbed> <testbed, if -s option> <num of docs to consider for MAP and NDCG analysis>"
)
exit(0)
option = sys.argv[1]
if option == '-a':
parameters.docs_to_consider = int(sys.argv[2])
testbed_names = [
'testbed1', 'testbed2', 'testbed3', 'testbed4', 'testbed5',
'testbed6', 'testbed7', 'testbed8', 'testbed9', 'testbed11',
'testbed12', 'testbed13', 'testbed14', 'testbed15', 'testbed16'
]
indicative_terms, top_k_documents = all_testbeds_optimise(
testbed_names)
print('+' * 100)
print('Optimal indicative terms : ' + str(indicative_terms))
print('Optimal top k documents : ' + str(top_k_documents))
print('+' * 100)
else:
parameters.docs_to_consider = int(sys.argv[3])
testbed_name = sys.argv[2]
queries = analyse.readin_testbed_queries(testbed_name)
max_MAP = 0
max_avg_NDCG = 0
optimal_indicative_terms = 0
optimal_top_k_documents = 0
# test 1-50 indicative terms and top k documents
for i in range(20):
parameters.number_indicative_terms = i + 1
for j in range(20):
parameters.number_top_k_documents = j + 1
current_MAP = 0
current_avg_NDCG = 0
query_number = 1
for query_sentence in queries:
result, accum, titles = analyse.submit_query(
testbed_name, query_sentence, True)
ap_score = ap.AP(result, query_number, testbed_name,
parameters.docs_to_consider)
ndcg_score = ndcg.NDCG(result, query_number, testbed_name,
parameters.docs_to_consider)
current_MAP += ap_score
current_avg_NDCG += ndcg_score
query_number += 1
current_MAP = current_MAP / len(queries)
current_avg_NDCG = current_avg_NDCG / len(queries)
print('=' * 100)
print('+' * 100)
print('Number of docs to consider : ' +
str(parameters.docs_to_consider))
print('Number of indicative terms : ' +
str(parameters.number_indicative_terms))
print('Number of top k documents : ' +
str(parameters.number_top_k_documents))
print('Optimal indicative terms : ' +
str(optimal_indicative_terms))
print('Optimal top k documents : ' +
str(optimal_top_k_documents))
print('+' * 100)
print('Calculated MAP : ' + str(current_MAP))
print('Calculated Average NDCG : ' + str(current_avg_NDCG))
print('*' * 100)
print('Current max MAP : ' + str(max_MAP))
print('Current max Average NDCG : ' + str(max_avg_NDCG))
print('=' * 100)
if (current_avg_NDCG + current_MAP) > (max_avg_NDCG + max_MAP):
max_MAP = current_MAP
max_avg_NDCG = current_avg_NDCG
optimal_indicative_terms = parameters.number_indicative_terms
optimal_top_k_documents = parameters.number_top_k_documents
print('#' * 100)
print('Final max MAP : ' + str(max_MAP))
print('Final max Average : ' + str(max_avg_NDCG))
print('Optimal indicative terms : ' + str(optimal_indicative_terms))
print('Optimal top k documents : ' + str(optimal_top_k_documents))
print('#' * 100)
