1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
|
#!/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 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(
"--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
|
