import scipy.sparse as sps
from scripts.scikit_ensemble.scikit_ensamble import Optimizer
from utils.definitions import *
from utils.datareader import Datareader
cat = 5
matrix = list()
from utils.definitions import load_obj
name = load_obj("name")
directory = ROOT_DIR + "/scripts/scikit_ensemble/offline/"
matrix_dict = load_obj("matrix_dict", path="")
m = list()
for n in name[cat-1]:
m.append(sps.load_npz(directory + matrix_dict[n]))
matrix.append(m)
dr = Datareader(verbose=False, mode = "offline", only_load="False")
opt = Optimizer(matrices_array=matrix[0], matrices_names=name[cat-1],
dr=dr, cat=cat, start=0, end=1)
del matrix
opt.run()
import itertools
def flatten(L):
return list(set([val for sublist in L for val in sublist]))
def reorder(dict, order):
assert len(dict) == len(order)
ret = [dict[k] for k in order]
return ret
if __name__ == '__main__':
name = load_obj("name")
mode = "online"
type = "unique"
print("[ Initizalizing Datereader ]")
dr = Datareader(verbose=False, mode=mode, only_load="False")
directory = ROOT_DIR + "/scripts/scikit_ensemble/" + mode + "/"
w = []
print("[ Loading weights ]")
for i in range(1, 11):
arg = load_obj("best/cat" + str(i) + "")
w.append(reorder(dict(arg[:len(arg) - 1][0]), name[i - 1]))
print("[ Loading matrix name ]")
if mode == "offline":
matrix_dict = load_obj("matrix_dict", path="")
best_score = 0
best_params = []
verbose = True
calls_constant = 60
start_index = (cat-1)*1000
end_index = cat*1000
global_counter=0
x0 = None
y0 = None
if os.path.isfile(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/memory/cat'+ str(cat)+'_y0_MEMORY.pkl') and \
os.path.isfile(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/memory/cat' + str(cat) + '_x0_MEMORY.pkl'):
x0 = load_obj('cat' + str(cat) + '_x0_MEMORY', path= ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/memory/')
y0 = load_obj('cat' + str(cat) + '_y0_MEMORY', path= ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/memory/')
global_counter = len(y0)
print("[ CAT"+str(cat)+" : RESUMING FROM RUN", global_counter, "]")
print("[ CAT "+str(cat)+": STARTING, NOW LOADING MATRICES ]")
matrices_names = read_params_dict(ROOT_DIR+'/bayesian_scikit/'+configuration_name+'/name_settings')[cat-1]
file_locations = read_params_dict(ROOT_DIR+'/bayesian_scikit/bayesian_common_files/file_locations_offline')
matrices_array = [norm( eurm_remove_seed( sps.load_npz(file_locations[x]), dr)[start_index:end_index]) for x in matrices_names ]
del dr
start_time=time.time()
space = [Real(0, 100, name=x) for x in matrices_names]
res = gp_minimize(objective_function, space,
import os, sys
from utils.definitions import ROOT_DIR, load_obj
configuration_name = sys.argv[1]
print("cat 1 ",load_obj(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/best_params/cat'+str(1)+'_params_dict'))
print("cat 2 ",load_obj(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/best_params/cat'+str(2)+'_params_dict'))
print("cat 3 ",load_obj(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/best_params/cat'+str(3)+'_params_dict'))
print("cat 4 ",load_obj(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/best_params/cat'+str(4)+'_params_dict'))
print("cat 5 ",load_obj(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/best_params/cat'+str(5)+'_params_dict'))
print("cat 6 ",load_obj(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/best_params/cat'+str(6)+'_params_dict'))
print("cat 7 ",load_obj(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/best_params/cat'+str(7)+'_params_dict'))
print("cat 8 ",load_obj(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/best_params/cat'+str(8)+'_params_dict'))
print("cat 9 ",load_obj(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/best_params/cat'+str(9)+'_params_dict'))
print("cat 10 ",load_obj(ROOT_DIR + '/bayesian_scikit/' + configuration_name + '/best_params/cat'+str(10)+'_params_dict'))
