append_timestamp = False
save_best_model = True
if use_saved_model:
n_reps = 1
acc_train_vect = {}
acc_test_vect = {}
output_filename = "naive_bayes_1"
prep = Preprocessing()
if config["one_hot_encoding"]:
prep.create_encoder(
prep.adapt_input(generator.generate_binary(config["n_valves"])))
if config["run_clean"] and not use_saved_model:
loader.clean(root_crt_model_folder)
def init_vect(vect):
for key in vect["data"]:
# print(key)
vect["data"][key].append(None)
vect["count"].append(None)
vect["files"].append(None)
def update_vect(vect, index, acc, count, dt, fsize, file1):
use_randomforest = True
prep = Preprocessing()
if use_randomforest:
root_crt_model_folder = config["root_model_container"] + "/dtree_multi"
output_filename = "dtree_2_multioutput"
else:
root_crt_model_folder = config["root_model_container"] + "/dtree"
output_filename = "dtree_1"
# output_filename = "eval_deep_3_rnn_random_"
# output_filename = "eval_deep_5_rnn_random_"
if config["one_hot_encoding"]:
binv = generator.generate_binary(config["n_valves"])
print("binv:")
print(binv)
binv = prep.adapt_input(binv)
print("adapted:")
print(binv)
# print("to list")
# print(prep.str_to_list(binv))
prep.create_encoder(binv)
# quit()
use_random_exp = True
use_matching_random_model = False
from_file = True