def main():
tuner = Tuner(param_space, objective, {'num_iteration': 30})
results = tuner.maximize()
print('best parameters:', results['best_params'])
print('best accuracy:', results['best_objective'])
assert results['best_objective'] > 0.93
def main():
if (sys.argv[1] != "nextpnr-ice40"):
print("ERROR: only nexpnr-ice40 is supported")
sys.exit(1)
space = {
'alpha': [x * .025 for x in range(1, 21)], # 0.025->0.5 (.25 step)
'beta': [x * .025 for x in range(20, 41)], # 0.500->1.0 (.25 step)
'critexp': range(1, 11, 1),
'tweight': range(1, 35, 5),
}
cfg = dict(num_iteration=20)
tuner = Tuner(space, evalpnr, cfg)
results = tuner.maximize()
print("Best clock: %s Mhz" % results["best_objective"])
print("Best parameters: [%s]" % results["best_params"])
# x_test = std_scaler.transform(x_test)
del x_train, x_dev # freeing up space
# tuner = svm_fits.train_mango_skcv(X=x_combined, Y=y_combined, n_splits=10)
param_space = {
# 'kernel': ['rbf', 'linear'],
# 'gamma': uniform(0.1, 4), # 0.1 to 4.1
'C': [1e-6, 1e-5, 1e-4, 1e-3, 0.01, 0.1] # loguniform(-6, 6) # 10^-7 to 10
}
# @scheduler.serial
def objectiveSVM(args_list):
results = []
for hyper_par in args_list:
svc = svm.LinearSVC(**hyper_par, max_iter=100000,
class_weight='balanced')
# svc = thunder(**hyper_par, max_iter=100000,
# class_weight='balanced')
result = cross_val_score(svc, x_combined, y_combined, scoring=my_scorer, n_jobs=-1, cv=10).mean()
results.append(result)
return results
tuner = Tuner(param_dict=param_space, objective=objectiveSVM)
results = tuner.maximize()
print('best hyper parameters:', results['best_params'])
print('best objective:', results['best_objective'])