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#!/usr/bin/env python3
"""analyze-log - Generate a model from performance benchmarks log files
foo
"""
import argparse
import dfatool.cli
import dfatool.utils
from dfatool.model import AnalyticModel
from dfatool.validation import CrossValidator
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 line in f:
m = re.search(r"\[::\] ([^|]*) [|] (.*)", line)
if m:
param_str = m.group(1)
attr_str = m.group(2)
param = dict(map(kv_to_param_i, param_str.split(" ")))
attr = dict(map(kv_to_param_f, attr_str.split(" ")))
observations.append(
{
"name": "Benchmark",
"param": param,
"attribute": attr,
}
)
return observations
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__
)
dfatool.cli.add_standard_arguments(parser)
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("logfile", type=str, help="Path to benchmark output")
args = parser.parse_args()
observations = parse_logfile(args.logfile)
by_name, parameter_names = dfatool.utils.observations_to_by_name(observations)
del observations
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.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 __name__ == "__main__":
main()
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