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tool: update analyse.py with the minisat-ml version
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1 changed files with 54 additions and 11 deletions
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@ -1,64 +1,107 @@
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#!/usr/bin/env python3
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import sys, csv, argparse
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import sys, csv, argparse, os
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def read_csv(f):
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with open(f) as fd:
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content = fd.readlines()
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return list(csv.DictReader(content))
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def analyze_file(f, potential_errors=False):
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def analyze_file(f, potential_errors=False, plot=None, mfleury=False):
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"""Analyze result file {f} (which should be a .csv file).
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Print per-solver analysis, and errors which happen quickly (true errors, not timeouts).
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If `plot` is provided, call `mkplot.py` to produce a nice plot of the results.
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If `mfleury` is true, use the alternative format provided by Mathias Fleury
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"""
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print(f"## analyze `{f}`")
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table = read_csv(f)
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print(f"read {len(table)} records")
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if not table: return
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provers = [x for x in table[0].keys() if ".time" not in x and x != "problem"]
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if mfleury:
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time_suffix = '_overall_time'
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res_suffix = '_result'
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provers = [x.split('_result')[0] for x in table[0].keys() if "_result" in x]
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else:
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time_suffix = '.time'
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res_suffix = ''
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provers = [x for x in table[0].keys() if ".time" not in x and x != "problem" and x != "status"]
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print(f"provers: {provers}")
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sat = {}
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unsat = {}
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unknown = {}
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error = {}
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total_time = {}
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if potential_errors:
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quick_errors = []
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for row in table:
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for prover in provers:
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res = row[prover]
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res = row[prover + res_suffix]
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time = row[prover + time_suffix]
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time = float(time) if time != 'NULL' else 0
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if res == 'unsat':
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unsat[prover] = 1 + unsat.get(prover, 0)
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total_time[prover] = time + total_time.get(prover,0)
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elif res == 'sat':
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sat[prover] = 1 + sat.get(prover, 0)
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elif res == 'unknown':
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total_time[prover] = time + total_time.get(prover,0)
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elif res == 'unknown' or res == 'timeout' or res == 'NULL':
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unknown[prover] = 1 + unknown.get(prover, 0)
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elif res == 'error':
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error[prover] = 1 + error.get(prover, 0)
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time = float(row[prover + '.time'])
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if potential_errors and time < 5:
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quick_errors.append((prover, row['problem'], time))
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else:
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print(f"unknown result for {prover} on {row}: {res}")
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for prover in provers:
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n = sat.get(prover,0)+unsat.get(prover,0)
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print(f"{prover:{12}}: sat {sat.get(prover,0):6}" \
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f" | unsat {unsat.get(prover,0):6}" \
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f" | solved {sat.get(prover,0)+unsat.get(prover,0):6}" \
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f" | solved {n:6}" \
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f" | unknown {unknown.get(prover,0):6}" \
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f" | error {error.get(prover,0):6}")
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f" | error {error.get(prover,0):6}" \
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f" | total-time {total_time.get(prover,0):14.3f}s" \
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f" | avg-time {0 if n==0 else total_time.get(prover,0)/n:8.3f}s")
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if potential_errors:
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for (prover,filename,time) in quick_errors:
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print(f"potential error: {prover} on `{filename}` after {time}")
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def main(files, potential_errors=False) -> ():
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if plot:
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print(f"plotting into {plot}…")
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import tempfile, json, subprocess
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with tempfile.TemporaryDirectory(prefix='analyze') as tmpdir:
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json_files = []
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# produce json files
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for prover in provers:
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filename = os.path.join(tmpdir, prover + '.json')
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json_files.append(filename)
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stats = {}
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for row in table:
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res = row[prover + res_suffix]
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ok = (res == 'unsat' or res=='sat')
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time = row[prover + time_suffix]
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time = float(time) if time != 'NULL' else 0
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stats[row['problem']] = {'status': ok, 'rtime': time}
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j = { 'preamble': {'program': prover}, 'stats': stats, }
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with open(filename, 'w') as out:
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#print(json.dumps(j, indent=2))
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out.write(json.dumps(j, indent=2))
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subprocess.call(["mkplot.py", "--save-to="+plot, "-b", "png"] + json_files)
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def main(files, **kwargs) -> ():
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for f in files:
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analyze_file(f, potential_errors=potential_errors)
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analyze_file(f, **kwargs)
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if __name__ == "__main__":
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p = argparse.ArgumentParser('analyze result files')
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p.add_argument('files', nargs='+', help='files to analyze')
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p.add_argument('--errors', dest='potential_errors', \
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action='store_true', help='detect potential errors')
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p.add_argument('--plot', dest='plot', help='produce a plot')
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p.add_argument('--mfleury', dest='mfleury', action='store_true',
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help="use mathias fleury's input format")
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args = p.parse_args()
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main(files=args.files, potential_errors=args.potential_errors)
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main(files=args.files, potential_errors=args.potential_errors,
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plot=args.plot, mfleury=args.mfleury)
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