vprint( verbose, "=========== " + basename.capitalize() +" Training cycle " + str(cycle) +" ================")
n_estimators = 10
if cycle:
n_estimators = int((time_budget / time_spent_last - 1) * n_estimators - time_stock)
# n_estimators = int(((time_budget / time_spent_last) - 1) * 5) # * 5 == aim to use 5/10 of the time budget
if n_estimators <= 0:
break
print("{} estimators".format(n_estimators))
K = D.info['target_num']
task = D.info['task']
autoML = OurAutoML(D.info).fit(D.data['X_train'], D.data['Y_train'], cv=2, n_estimators=n_estimators)
vprint( verbose, "[+] Fitting success, time spent so far %5.2f sec" % (time.time() - start))
# Make predictions
Y_valid = autoML.predict(D.data['X_valid'])
Y_test = autoML.predict(D.data['X_test'])
print("score: {} ({} s)".format(autoML.scores.mean(), "%5.2f"%(time.time() - start)))
vprint( verbose, "[+] Prediction success, time spent so far %5.2f sec" % (time.time() - start))
# Write results
filename_valid = basename + '_valid_' + str(cycle).zfill(3) + '.predict'
data_io.write(os.path.join(output_dir,filename_valid), Y_valid)
filename_test = basename + '_test_' + str(cycle).zfill(3) + '.predict'
data_io.write(os.path.join(output_dir,filename_test), Y_test)
vprint( verbose, "[+] Results saved, time spent so far %5.2f sec" % (time.time() - start))
time_spent = time.time() - start
vprint( verbose, "[+] End cycle, remaining time %5.2f sec" % (time_budget-time_spent))
cycle += 1
time_spent_last = time.time() - begin
time_budget = time_budget - time_spent_last # Remove time spent so far
