app = "_" + str(time.time())
model_file_raw += app
model_file = model_file_raw + ".skl"
# X = x[:, i]
X = x
# print(np.shape(x))
# print(np.shape(y))
n_train_percent = config["train_percent"]
x_train, y_train = classifiers.split_dataset_train(
X, y, n_train_percent)
x_test, y_test = classifiers.split_dataset_test(X, y, n_train_percent)
dt = 0
if not use_saved_model:
tstart = time.time()
model = classifiers.create_svm_multiclass()
model, acc = classifiers.train_decision_tree(
model, x_train, y_train)
dt = time.time() - tstart
else:
model = model_loader.load_sklearn_model(model_file)
model, acc, diff, total, _ = classifiers.predict_decision_tree(
model, x_train, y_train, False)
# if y2 is None:
# y2 = [y21]
# # print(y2)
# # print([y21])
# else:
# y2 = np.append(y2, [y21], axis=0)
# y = y2
# print(y)
# quit(0)
x_train, y_train = classifiers.split_dataset_train(x, y, train_percent)
x_eval, y_eval = classifiers.split_dataset_test(x, y, train_percent)
print("end select data")
quit(0)
##
sizex = np.shape(x_train)
top_acc = 0
top_model_filename = None
# run multiple evaluations (each training may return different results in terms of accuracy)
for i in range(n_reps):
print("evaluating model rep: " + str(i) + "/" + str(n_reps))
# session = K.get_session()