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
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
|
#!/usr/bin/env python3
"""analyze-log - Generate a model from performance benchmarks log files
foo
"""
import argparse
import dfatool.cli
import dfatool.plotter
import dfatool.utils
from dfatool.model import AnalyticModel
from dfatool.validation import CrossValidator
from functools import reduce
import re
def kv_to_param(kv_str, cast):
key, value = kv_str.split("=")
value = cast(value)
return key, value
def kv_to_param_f(kv_str):
return kv_to_param(kv_str, dfatool.utils.soft_cast_float)
def kv_to_param_i(kv_str):
return kv_to_param(kv_str, dfatool.utils.soft_cast_int)
def parse_logfile(filename):
observations = list()
with open(filename, "r") as f:
for lineno, line in enumerate(f):
m = re.search(r"\[::\] *([^|]*?) *[|] *([^|]*?) *[|] *(.*)", line)
if m:
name_str = m.group(1)
param_str = m.group(2)
attr_str = m.group(3)
try:
param = dict(map(kv_to_param_i, param_str.split(" ")))
attr = dict(map(kv_to_param_f, attr_str.split(" ")))
observations.append(
{
"name": name_str,
"param": param,
"attribute": attr,
}
)
except ValueError:
print(
f"Error parsing {filename}: invalid key-value pair in line {lineno+1}"
)
print(f"Offending entry:\n{line}")
raise
return observations
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
)
dfatool.cli.add_standard_arguments(parser)
parser.add_argument(
"--plot-unparam",
metavar="<name>:<attribute>:<Y axis label>[;<name>:<attribute>:<label>;...]",
type=str,
help="Plot all mesurements for <name> <attribute> without regard for parameter values. "
"X axis is measurement number/id.",
)
parser.add_argument(
"--plot-param",
metavar="<name> <attribute> <parameter> [gplearn function][;<name> <attribute> <parameter> [function];...])",
type=str,
help="Plot measurements for <name> <attribute> by <parameter>. "
"X axis is parameter value. "
"Plots the model function as one solid line for each combination of non-<parameter> parameters. "
"Also plots the corresponding measurements. "
"If gplearn function is set, it is plotted using dashed lines.",
)
parser.add_argument(
"--show-model",
choices=["static", "paramdetection", "param", "all"],
action="append",
default=list(),
help="static: show static model values as well as parameter detection heuristic.\n"
"paramdetection: show stddev of static/lut/fitted model\n"
"param: show parameterized model functions and regression variable values\n"
"all: all of the above\n",
)
parser.add_argument(
"--show-quality",
choices=["table", "summary", "all"],
action="append",
default=list(),
help="table: show static/fitted/lut SMAPE and MAE for each name and attribute.\n"
"summary: show static/fitted/lut SMAPE and MAE for each attribute, averaged over all states/transitions.\n"
"all: all of the above.\n",
)
parser.add_argument(
"--force-tree",
action="store_true",
help="Build decision tree without checking for analytic functions first",
)
parser.add_argument(
"logfiles", nargs="+", type=str, help="Path to benchmark output"
)
args = parser.parse_args()
if args.filter_param:
args.filter_param = list(
map(lambda x: x.split("="), args.filter_param.split(","))
)
else:
args.filter_param = list()
observations = reduce(lambda a, b: a + b, map(parse_logfile, args.logfiles))
by_name, parameter_names = dfatool.utils.observations_to_by_name(observations)
del observations
dfatool.utils.filter_aggregate_by_param(by_name, parameter_names, args.filter_param)
if args.param_shift:
param_shift = dfatool.cli.parse_param_shift(args.param_shift)
dfatool.utils.shift_param_in_aggregate(by_name, parameter_names, param_shift)
if args.normalize_nfp:
norm = dfatool.cli.parse_nfp_normalization(args.normalize_nfp)
dfatool.utils.normalize_nfp_in_aggregate(by_name, norm)
model = AnalyticModel(
by_name,
parameter_names,
force_tree=args.force_tree,
)
if args.info:
dfatool.cli.print_info_by_name(model, by_name)
if args.export_pgf_unparam:
dfatool.cli.export_pgf_unparam(model, args.export_pgf_unparam)
if args.plot_unparam:
for kv in args.plot_unparam.split(";"):
state_or_trans, attribute, ylabel = kv.split(":")
fname = "param_y_{}_{}.pdf".format(state_or_trans, attribute)
dfatool.plotter.plot_y(
model.by_name[state_or_trans][attribute],
xlabel="measurement #",
ylabel=ylabel,
# output=fname,
)
if args.cross_validate:
xv_method, xv_count = args.cross_validate.split(":")
xv_count = int(xv_count)
xv = CrossValidator(
AnalyticModel,
by_name,
parameter_names,
force_tree=args.force_tree,
max_std=max_std,
compute_stats=not args.skip_param_stats,
)
xv.parameter_aware = args.parameter_aware_cross_validation
else:
xv_method = None
static_model = model.get_static()
try:
lut_model = model.get_param_lut()
except RuntimeError as e:
if args.force_tree:
# this is to be expected
logging.debug(f"Skipping LUT model: {e}")
else:
logging.warning(f"Skipping LUT model: {e}")
lut_model = None
param_model, param_info = model.get_fitted()
static_quality = model.assess(static_model)
analytic_quality = model.assess(param_model)
if lut_model:
lut_quality = model.assess(lut_model)
else:
lut_quality = None
if "static" in args.show_model or "all" in args.show_model:
print("--- static model ---")
for name in model.names:
for attribute in model.attributes(name):
dfatool.cli.print_static(model, static_model, name, attribute)
if "table" in args.show_quality or "all" in args.show_quality:
if xv_method is not None:
print(f"Model error after cross validation ({xv_method}, {xv_count}):")
else:
print("Model error on training data:")
dfatool.cli.model_quality_table(
["static", "parameterized", "LUT"],
[static_quality, analytic_quality, lut_quality],
[None, param_info, None],
)
if args.plot_param:
for kv in args.plot_param.split(";"):
try:
state_or_trans, attribute, param_name = kv.split(":")
except ValueError:
print(
"Usage: --plot-param='state_or_trans:attribute:param_name'",
file=sys.stderr,
)
sys.exit(1)
dfatool.plotter.plot_param(
model,
state_or_trans,
attribute,
model.param_index(param_name),
)
if __name__ == "__main__":
main()
|
