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#!/usr/bin/env python3
import getopt
import re
import sys
from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate
opt = dict()
def model_quality_table(result_lists, info_list):
for state_or_tran in result_lists[0]["by_name"].keys():
for key in result_lists[0]["by_name"][state_or_tran].keys():
buf = "{:20s} {:15s}".format(state_or_tran, key)
for i, results in enumerate(result_lists):
results = results["by_name"]
info = info_list[i]
buf += " ||| "
if info == None or info(state_or_tran, key):
result = results[state_or_tran][key]
if "smape" in result:
buf += "{:6.2f}% / {:9.0f}".format(
result["smape"], result["mae"]
)
else:
buf += "{:6} {:9.0f}".format("", result["mae"])
else:
buf += "{:6}----{:9}".format("", "")
print(buf)
def combo_model_quality_table(result_lists, info_list):
for state_or_tran in result_lists[0][0]["by_name"].keys():
for key in result_lists[0][0]["by_name"][state_or_tran].keys():
for sub_result_lists in result_lists:
buf = "{:20s} {:15s}".format(state_or_tran, key)
for i, results in enumerate(sub_result_lists):
results = results["by_name"]
info = info_list[i]
buf += " ||| "
if info == None or info(state_or_tran, key):
result = results[state_or_tran][key]
if "smape" in result:
buf += "{:6.2f}% / {:9.0f}".format(
result["smape"], result["mae"]
)
else:
buf += "{:6} {:9.0f}".format("", result["mae"])
else:
buf += "{:6}----{:9}".format("", "")
print(buf)
if __name__ == "__main__":
ignored_trace_indexes = []
discard_outliers = None
try:
raw_opts, args = getopt.getopt(
sys.argv[1:], "", "plot ignored-trace-indexes= discard-outliers=".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"])
except getopt.GetoptError as err:
print(err)
sys.exit(2)
raw_data = RawData(args)
preprocessed_data = raw_data.get_preprocessed_data()
by_name, parameters, arg_count = pta_trace_to_aggregate(
preprocessed_data, ignored_trace_indexes
)
m1 = PTAModel(
by_name,
parameters,
arg_count,
traces=preprocessed_data,
ignore_trace_indexes=ignored_trace_indexes,
)
m2 = PTAModel(
by_name,
parameters,
arg_count,
traces=preprocessed_data,
ignore_trace_indexes=ignored_trace_indexes,
discard_outliers=discard_outliers,
)
print("--- simple static model ---")
static_m1 = m1.get_static()
static_m2 = m2.get_static()
# for state in model.states():
# print('{:10s}: {:.0f} µW ({:.2f})'.format(
# state,
# static_model(state, 'power'),
# model.generic_param_dependence_ratio(state, 'power')))
# for param in model.parameters():
# print('{:10s} dependence on {:15s}: {:.2f}'.format(
# '',
# param,
# model.param_dependence_ratio(state, 'power', param)))
# for trans in model.transitions():
# print('{:10s}: {:.0f} / {:.0f} / {:.0f} pJ ({:.2f} / {:.2f} / {:.2f})'.format(
# trans, static_model(trans, 'energy'),
# static_model(trans, 'rel_energy_prev'),
# static_model(trans, 'rel_energy_next'),
# model.generic_param_dependence_ratio(trans, 'energy'),
# model.generic_param_dependence_ratio(trans, 'rel_energy_prev'),
# model.generic_param_dependence_ratio(trans, 'rel_energy_next')))
# print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration')))
static_q1 = m1.assess(static_m1)
static_q2 = m2.assess(static_m2)
static_q12 = m1.assess(static_m2)
print("--- LUT ---")
lut_m1 = m1.get_param_lut()
lut_m2 = m2.get_param_lut()
lut_q1 = m1.assess(lut_m1)
lut_q2 = m2.assess(lut_m2)
lut_q12 = m1.assess(lut_m2)
print("--- param model ---")
param_m1, param_i1 = m1.get_fitted()
for state in m1.states():
for attribute in ["power"]:
if param_i1(state, attribute):
print(
"{:10s}: {}".format(
state, param_i1(state, attribute)["function"]._model_str
)
)
print(
"{:10s} {}".format(
"", param_i1(state, attribute)["function"]._regression_args
)
)
for trans in m1.transitions():
for attribute in [
"energy",
"rel_energy_prev",
"rel_energy_next",
"duration",
"timeout",
]:
if param_i1(trans, attribute):
print(
"{:10s}: {:10s}: {}".format(
trans,
attribute,
param_i1(trans, attribute)["function"]._model_str,
)
)
print(
"{:10s} {:10s} {}".format(
"", "", param_i1(trans, attribute)["function"]._regression_args
)
)
param_m2, param_i2 = m2.get_fitted()
for state in m2.states():
for attribute in ["power"]:
if param_i2(state, attribute):
print(
"{:10s}: {}".format(
state, param_i2(state, attribute)["function"]._model_str
)
)
print(
"{:10s} {}".format(
"", param_i2(state, attribute)["function"]._regression_args
)
)
for trans in m2.transitions():
for attribute in [
"energy",
"rel_energy_prev",
"rel_energy_next",
"duration",
"timeout",
]:
if param_i2(trans, attribute):
print(
"{:10s}: {:10s}: {}".format(
trans,
attribute,
param_i2(trans, attribute)["function"]._model_str,
)
)
print(
"{:10s} {:10s} {}".format(
"", "", param_i2(trans, attribute)["function"]._regression_args
)
)
analytic_q1 = m1.assess(param_m1)
analytic_q2 = m2.assess(param_m2)
analytic_q12 = m1.assess(param_m2)
model_quality_table([static_q1, analytic_q1, lut_q1], [None, param_i1, None])
model_quality_table([static_q2, analytic_q2, lut_q2], [None, param_i2, None])
model_quality_table([static_q12, analytic_q12, lut_q12], [None, param_i2, None])
combo_model_quality_table(
[
[static_q1, analytic_q1, lut_q1],
[static_q2, analytic_q2, lut_q2],
[static_q12, analytic_q12, lut_q12],
],
[None, param_i1, None],
)
sys.exit(0)
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