def __best_solution(training_set, test_set, knn_function, solutions, adaptative=False):
n_features = len(solutions[0][0])
best = (0, ([1.0]*n_features, [1]*n_features, 1))
for s in solutions:
hit_rate = training_machine(training_set, test_set, knn_function, s, adaptative)
if hit_rate > best[0]:
best = (hit_rate, s)
return best
def __best_solution(training_set, test_set, knn_function, solutions, adaptative=False):
n_features = len(solutions[0][0])
best = (0, ([1.0]*n_features, [1]*n_features, 1))
for s in solutions:
hit_rate = training_machine(training_set, test_set, knn_function, s, adaptative)
if hit_rate > best[0]:
best = (hit_rate, s)
return best
def __cost(training_set, test_set, knn_function, solution, adaptative=False):
"""
Calculates the cost of a given solution.
In this case, calculates the accuracy rate of a k-NN.
@type training_set: dict
@param training_set: the training dataset
@type test_set: dict
@param test_set: the test dataset
@type knn_function: function
@param knn_function: the function used to classify the examples
@type solution: tuple
@param solution: the solution
@type adaptative: bool
@param adaptative: indicates when to use adaptative distance
@rtype: float
@returns: the cost of the given solution
"""
# Dado a solucao (configuracao de pesos/caracteristicas), transforma os , calcula o k-NN e retorna a taxa de acerto
return training_machine(training_set, test_set, knn_function, solution, adaptative)
def __cost(training_set, test_set, knn_function, solution, adaptative=False):
"""
Calculates the cost of a given solution.
In this case, calculates the accuracy rate of a k-NN.
@type training_set: dict
@param training_set: the training dataset
@type test_set: dict
@param test_set: the test dataset
@type knn_function: function
@param knn_function: the function used to classify the examples
@type solution: tuple
@param solution: the solution
@type adaptative: bool
@param adaptative: indicates when to use adaptative distance
@rtype: float
@returns: the cost of the given solution
"""
# Dado a solucao (configuracao de pesos/caracteristicas), transforma os , calcula o k-NN e retorna a taxa de acerto
return training_machine(training_set, test_set, knn_function, solution, adaptative)
return best
if __name__ == '__main__':
# Tabu search Adaptive
for path in ['liver/selected/drop3', 'liver/selected/hmn_ei',
'liver/selected/ib2', 'liver/selected/icf', 'liver/selected/mldb',
'liver/selected/oss', 'liver/folds/original']:
path = 'datasets/' + path
for e in range(5):
n_features = 6
solution = ([1.0]*n_features, [1]*n_features, 1)
training_set = get_data(path + '/training_%i' % e)
test_set = get_data(path + '/test_%i' % e)
# Tabu search Adaptive
with open(path + '/ts-adaptive-%i.log' % e,'w') as f:
initial = clock()
print 'TS Adaptive Distance'
best_solution = tabu_search(training_set, test_set, knn_euclidian, solution, 100, f, adaptative=True)
f.write('best solution: %s\n' % str(best_solution))
middle = clock()
print 'Hits for the best solution'
hits = training_machine(training_set, test_set, knn_euclidian, best_solution)
print hits
final = clock()
f.write('Hits for the best solution: %s\n' % hits)
f.write('TS Spent time: %s\n' % str(middle - initial))
f.write('k-NN Spent time: %s\n' % str(final - middle))
path = 'datasets/' + path
for e in range(5):
n_features = 6
solution = ([1.0] * n_features, [1] * n_features, 1)
training_set = get_data(path + '/training_%i' % e)
test_set = get_data(path + '/test_%i' % e)
# Tabu search Adaptive
with open(path + '/ts-adaptive-%i.log' % e, 'w') as f:
initial = clock()
print 'TS Adaptive Distance'
best_solution = tabu_search(training_set,
test_set,
knn_euclidian,
solution,
100,
f,
adaptative=True)
f.write('best solution: %s\n' % str(best_solution))
middle = clock()
print 'Hits for the best solution'
hits = training_machine(training_set, test_set, knn_euclidian,
best_solution)
print hits
final = clock()
f.write('Hits for the best solution: %s\n' % hits)
f.write('TS Spent time: %s\n' % str(middle - initial))
f.write('k-NN Spent time: %s\n' % str(final - middle))
