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#!/usr/bin/env python3
from dfatool.functions import SplitFunction, AnalyticFunction, StaticFunction
def print_static(model, static_model, name, attribute):
unit = " "
if attribute == "power":
unit = "µW"
elif attribute == "duration":
unit = "µs"
elif attribute == "substate_count":
unit = "su"
print(
"{:10s}: {:.0f} {:s} ({:.2f})".format(
name,
static_model(name, attribute),
unit,
model.attr_by_name[name][attribute].stats.generic_param_dependence_ratio(),
)
)
for param in model.parameters:
print(
"{:10s} dependence on {:15s}: {:.2f}".format(
"",
param,
model.attr_by_name[name][attribute].stats.param_dependence_ratio(param),
)
)
def print_analyticinfo(prefix, info):
empty = ""
print(f"{prefix}: {info.model_function}")
print(f"{empty:{len(prefix)}s} {info.model_args}")
def print_splitinfo(param_names, info, prefix=""):
if type(info) is SplitFunction:
for k, v in info.child.items():
if info.param_index < len(param_names):
param_name = param_names[info.param_index]
else:
param_name = f"arg{info.param_index - len(param_names)}"
print_splitinfo(param_names, v, f"{prefix} {param_name}={k}")
elif type(info) is AnalyticFunction:
print_analyticinfo(prefix, info)
elif type(info) is StaticFunction:
print(f"{prefix}: {info.value}")
else:
print(f"{prefix}: UNKNOWN")
def print_model_size(model):
for name in model.names:
for attribute in model.attributes(name):
try:
num_nodes = model.attr_by_name[name][
attribute
].model_function.get_number_of_nodes()
max_depth = model.attr_by_name[name][
attribute
].model_function.get_max_depth()
print(
f"{name:15s} {attribute:20s}: {num_nodes:6d} nodes @ {max_depth:3d} max depth"
)
except AttributeError:
print(
f"{name:15s} {attribute:20s}: {model.attr_by_name[name][attribute].model_function}"
)
def format_quality_measures(result):
if "smape" in result:
return "{:6.2f}% / {:9.0f}".format(result["smape"], result["mae"])
else:
return "{:6} {:9.0f}".format("", result["mae"])
def model_quality_table(header, result_lists, info_list):
print(
"{:20s} {:15s} {:19s} {:19s} {:19s}".format(
"key",
"attribute",
header[0].center(19),
header[1].center(19),
header[2].center(19),
)
)
for state_or_tran in result_lists[0].keys():
for key in result_lists[0][state_or_tran].keys():
buf = "{:20s} {:15s}".format(state_or_tran, key)
for i, results in enumerate(result_lists):
info = info_list[i]
buf += " ||| "
if (
info is None
or (
key != "energy_Pt"
and type(info(state_or_tran, key)) is not StaticFunction
)
or (
key == "energy_Pt"
and (
type(info(state_or_tran, "power")) is not StaticFunction
or type(info(state_or_tran, "duration"))
is not StaticFunction
)
)
):
result = results[state_or_tran][key]
buf += format_quality_measures(result)
else:
buf += "{:7}----{:8}".format("", "")
print(buf)
def add_standard_arguments(parser):
parser.add_argument(
"--export-dref",
metavar="FILE",
type=str,
help="Export model and model quality to LaTeX dataref file",
)
parser.add_argument(
"--show-model-size",
action="store_true",
help="Show model size (e.g. regression tree height and node count)",
)
parser.add_argument(
"--cross-validate",
metavar="<method>:<count>",
type=str,
help="Perform cross validation when computing model quality. "
"Only works with --show-quality=table at the moment.",
)
parser.add_argument(
"--parameter-aware-cross-validation",
action="store_true",
help="Perform parameter-aware cross-validation: ensure that parameter values (and not just observations) are mutually exclusive between training and validation sets.",
)
parser.add_argument(
"--param-shift",
metavar="<key>=<+|-|*|/><value>;...",
type=str,
help="Adjust parameter values before passing them to model generation",
)
def parse_param_shift(raw_param_shift):
shift_list = list()
for shift_pair in raw_param_shift.split(";"):
param_name, param_shift = shift_pair.split("=")
if param_shift.startswith("+"):
param_shift_value = float(param_shift[1:])
param_shift_function = lambda p: p + param_shift_value
elif param_shift.startswith("-"):
param_shift_value = float(param_shift[1:])
param_shift_function = lambda p: p - param_shift_value
elif param_shift.startswith("*"):
param_shift_value = float(param_shift[1:])
param_shift_function = lambda p: p * param_shift_value
elif param_shift.startswith("/"):
param_shift_value = float(param_shift[1:])
param_shift_function = lambda p: p / param_shift_value
else:
raise ValueError(f"Unsupported shift operation {param_name}={param_shift}")
shift_list.append((param_name, param_shift_function))
return shift_list
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