print("\tperformance: exec_time, comp_time")
print("\tnumber of samples: 25, 50, 100")
print("\t#runs: 10, 100")
print("\talgorithm: ceal")
exit()
wf = sys.argv[1]
perfn = sys.argv[2]
num_smpl = int(sys.argv[3])
num_run = int(sys.argv[4])
algo = sys.argv[5]
dir_name = '../plot/pct_repl/' + wf + '_' + perfn + '/'
if (wf == 'lv'):
lmp_mdl = mdlr.train_mdl(sp.df_lmp, sp.lmp_confn, perfn)
vr_mdl = mdlr.train_mdl(sp.df_vr, sp.vr_confn, perfn)
cpnt_mdls = [lmp_mdl, vr_mdl]
cpnt_confns = (sp.lmp_confn, sp.vr_confn)
confn = sp.lv_confn
if (perfn == 'exec_time'):
if (num_smpl == 50):
if (algo == 'ceal'):
pct_rand = 0.1
num_iter = 7
else:
print("Error: unknown algorithm!")
exit()
else: # 100 samples
if (algo == 'ceal'):
pct_rand = 0.05
import numpy as np
import pandas as pd
import xgboost as xgb
import sample as sp
import modeler as mdlr
gs_mdl = mdlr.train_mdl(sp.df_gs, sp.gs_confn, 'exec_time')
pdf_mdl = mdlr.train_mdl(sp.df_pdf, sp.pdf_confn, 'exec_time')
cpnt_mdls = [gs_mdl, pdf_mdl, sp.df_gplot, sp.df_pplot]
for i in range(len(cpnt_mdls)):
if isinstance(cpnt_mdls[i], xgb.sklearn.XGBRegressor):
print("xgb.sklearn.XGBRegressor")
elif isinstance(cpnt_mdls[i], float):
print("float")
elif isinstance(cpnt_mdls[i], pd.core.frame.DataFrame):
print("pd.core.frame.DataFrame")
else:
print(f"Unknown type of cpnt_mdls[{i}]")
print(3 * np.ones(5))