def main():
fileW = FromFinessFileMLR.createAnOutputFile()
model = mlr.MLR()
#Number of descriptor should be 396 and number of population should be 50 or more
numOfPop = 50
numOfFea = 396
unfit = 1000
# Final model requirements
R2req_train = .6
R2req_validate = .5
R2req_test = .5
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY = FromDataFileMLR.getAllOfTheData(
)
TrainX, ValidateX, TestX = FromDataFileMLR.rescaleTheData(
TrainX, ValidateX, TestX)
unfit = 1000
fittingStatus = unfit
"""Create a population based on the number of features selected, in this case 10, from the pool of features"""
population = DifferentialEvolution.Create_A_Population(numOfPop, numOfFea)
fittingStatus, fitness = FromFinessFileMLR.validate_model(model,fileW, population, \
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY)
def main():
np.random.seed()
# initialize objects
fileW = createAnOutputFile()
model = mlr.MLR()
# load in data from files
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY = FromDataFileMLR.getAllOfTheData(
)
TrainX, ValidateX, TestX = FromDataFileMLR.rescaleTheData(
TrainX, ValidateX, TestX)
# DE_BPSO algorithm
velocity = create_initial_velocity()
population = create_initial_population(velocity, Lambda=0.01)
x, fitness = FromFinessFileMLR.validate_model(model,fileW, population, \
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY)
local_best_matrix, local_fitness = create_initial_local_best_matrix(
population, fitness)
create_initial_global_best_row(local_best_matrix, local_fitness)
evolve_population(population, fitness, velocity, local_best_matrix, local_fitness, \
model, fileW, TrainX, TrainY, ValidateX, ValidateY, TestX, TestY)
def evolve_population(initial_population, initial_fitness, initial_velocity,
initial_local_best_matrix, initial_local_fitness, model,
fileW, TrainX, TrainY, ValidateX, ValidateY, TestX,
TestY):
print('gen #' + str(0) + '\t\t min fit: ' + str(initial_fitness.min()) +
'\t\t avg fit: ' + str(np.average(initial_fitness)))
alpha = 0.5
for i in range(1, numOfGens):
velocity = update_velocity(initial_velocity, initial_population)
population = create_new_population(numOfPop, numOfFea,
initial_population, velocity,
initial_local_best_matrix, alpha, i)
x, fitness = FromFinessFileMLR.validate_model(model, fileW, population, \
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY)
local_best_matrix, local_fitness = update_local_best_matrix(
population, fitness, initial_local_best_matrix,
initial_local_fitness)
update_global_best_row(local_best_matrix, local_fitness)
alpha = alpha - (0.17 / numOfGens)
print('gen #' + str(i) + '\t\t min fit: ' + str(fitness.min()) +
'\t\t avg fit: ' + str(np.average(fitness)))
# Final model requirements
R2req_train = .6
R2req_validate = .5
R2req_test = .5
alpha = 0.5
beta = 0.004
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY = FromDataFileMLR.getAllOfTheData(
)
TrainX, ValidateX, TestX = FromDataFileMLR.rescaleTheData(
TrainX, ValidateX, TestX)
population = BPSO.Create_A_Population(numOfPop, numOfFea)
""" Get fitness"""
fitness = FromFinessFileMLR.validate_model(model, fileW, population, \
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY)
"""Initialize velocity"""
initial_velocity = BPSO.create_initial_velocity(numOfPop, numOfFea)
#print str(shape(initial_velocity))
"""Initialize Local Best Matrix (Same as Initial Population)"""
local_best_matrix = population
"""Create Global best row"""
global_best_row_index = argmin(fitness)
global_best_row_fitness = fitness[global_best_row_index]
global_best_row = population[global_best_row_index]
#global_best_row=population[argmin(fitness)]
generations_to_run = 2000
print "Starting program"
def evaluate_population(model, fileW, population, trainX, trainY, validateX,
validateY, testX, testY):
fittingStatus, fitness = FromFinessFileMLR.validate_model(
model, fileW, population, trainX, trainY, validateX, validateY, testX,
testY)
return fitness
R2req_train = .6
R2req_validate = .5
R2req_test = .5
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY = FromDataFileMLR.getAllOfTheData(
)
TrainX, ValidateX, TestX = FromDataFileMLR.rescaleTheData(
TrainX, ValidateX, TestX)
unfit = 1000
fittingStatus = unfit
""" Generate population with 385 features and 50 pop"""
population = DifferentialEvolution.Create_A_Population(numOfPop, numOfFea)
""" Get fitness for the population"""
fittingStatus, fitness = FromFinessFileMLR.validate_model(model, fileW, population, \
TrainX, TrainY, ValidateX, ValidateY, TestX, TestY)
"""Create functions in the Differential Evolution class for the following: """
"""grab best model-fitness (row) and move it to new pop in same index"""
#DifferentialEvolution.create_DE_population(numOfPop, numOfFea,fitness,population)
generations_to_run = 20000
fit = fitness
iterations_since_best_fitness_has_changed = 0
for i in range(0, generations_to_run):
print "Running Generation " + str(i)
if iterations_since_best_fitness_has_changed == 500:
print "Program stopped - the best fitness has not changed in 500 generations"
break
fitness = fit
best_fitness_index = argmin(fitness)
pop, fit = DifferentialEvolution.create_DE_population(