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#!/usr/bin/env python3
import getopt
import re
import sys
from dfatool.dfatool import RawData, pta_trace_to_aggregate
from dfatool.model import PTAModel
opt = dict()
def get_file_groups(args):
groups = []
index_low = 0
while ":" in args[index_low:]:
index_high = args[index_low:].index(":") + index_low
groups.append(args[index_low:index_high])
index_low = index_high + 1
groups.append(args[index_low:])
return groups
if __name__ == "__main__":
ignored_trace_indexes = []
discard_outliers = None
safe_functions_enabled = False
function_override = {}
show_models = []
show_quality = []
try:
optspec = (
"plot-unparam= plot-param= show-models= show-quality= "
"ignored-trace-indexes= discard-outliers= function-override= "
"with-safe-functions"
)
raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" "))
for option, parameter in raw_opts:
optname = re.sub(r"^--", "", option)
opt[optname] = parameter
if "ignored-trace-indexes" in opt:
ignored_trace_indexes = list(
map(int, opt["ignored-trace-indexes"].split(","))
)
if 0 in ignored_trace_indexes:
print("[E] arguments to --ignored-trace-indexes start from 1")
if "discard-outliers" in opt:
discard_outliers = float(opt["discard-outliers"])
if "function-override" in opt:
for function_desc in opt["function-override"].split(";"):
state_or_tran, attribute, *function_str = function_desc.split(" ")
function_override[(state_or_tran, attribute)] = " ".join(
function_str
)
if "show-models" in opt:
show_models = opt["show-models"].split(",")
if "show-quality" in opt:
show_quality = opt["show-quality"].split(",")
if "with-safe-functions" in opt:
safe_functions_enabled = True
except getopt.GetoptError as err:
print(err)
sys.exit(2)
score_absolute = 0
score_relative = 0
for file_group in get_file_groups(args):
print("")
print("{}:".format(" ".join(file_group)))
raw_data = RawData(file_group)
preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
by_name, parameters, arg_count = pta_trace_to_aggregate(
preprocessed_data, ignored_trace_indexes
)
model = PTAModel(
by_name,
parameters,
arg_count,
traces=preprocessed_data,
ignore_trace_indexes=ignored_trace_indexes,
discard_outliers=discard_outliers,
function_override=function_override,
verbose=False,
)
lut_quality = model.assess(model.get_param_lut())
for trans in model.transitions():
absolute_quality = lut_quality["by_name"][trans]["energy"]
relative_quality = lut_quality["by_name"][trans]["rel_energy_prev"]
if absolute_quality["mae"] < relative_quality["mae"]:
best = "absolute"
score_absolute += 1
else:
best = "relative"
score_relative += 1
print(
"{:20s}: {:s} (diff {:.0f} / {:.2f}%, abs {:.0f} / {:.2f}%, rel {:.0f} / {:.2f}%)".format(
trans,
best,
abs(absolute_quality["mae"] - relative_quality["mae"]),
abs(absolute_quality["mae"] - relative_quality["mae"])
* 100
/ max(absolute_quality["mae"], relative_quality["mae"]),
absolute_quality["mae"],
absolute_quality["smape"],
relative_quality["mae"],
relative_quality["smape"],
)
)
sys.exit(0)
|
