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feat: Use seeding performance in score for Optuna and Orion automatic tuning #2662

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merged 10 commits into from
Feb 5, 2024
Merged

feat: Use seeding performance in score for Optuna and Orion automatic tuning #2662

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84e10e5
add seeding performance in optuna score
LuisFelipeCoelho Nov 14, 2023
fb6b11d
same for orion
LuisFelipeCoelho Nov 14, 2023
d854d29
Update Examples/Scripts/Optimization/Optuna_tuning.py
LuisFelipeCoelho Nov 22, 2023
5a1537f
Update Examples/Scripts/Optimization/Optuna_tuning.py
LuisFelipeCoelho Nov 22, 2023
8b2c3bb
outFileName -> outDirName
LuisFelipeCoelho Nov 22, 2023
e734ece
Merge branch 'main' into uptuna_seeding
LuisFelipeCoelho Jan 19, 2024
e921735
change logic
LuisFelipeCoelho Jan 19, 2024
8d986ff
Merge branch 'uptuna_seeding' of https://github.com/LuisFelipeCoelho/…
LuisFelipeCoelho Jan 19, 2024
0819498
refactor
LuisFelipeCoelho Jan 19, 2024
3a77541
Merge branch 'main' into uptuna_seeding
kodiakhq[bot] Feb 5, 2024
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41 changes: 29 additions & 12 deletions Examples/Scripts/Optimization/Optuna_tuning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ def __init__(self, k_dup, k_time):
self.k_dup = k_dup
self.k_time = k_time

def __call__(self, trial):
def __call__(self, trial, ckf_perf=True):
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params = []

maxSeedsPerSpM = trial.suggest_int("maxSeedsPerSpM", 0, 10)
Expand Down Expand Up @@ -110,24 +110,41 @@ def __call__(self, trial):
"deltaRMax",
]

outputDir = Path(srcDir / "Output_CKF")
outputfile = srcDir / "Output_CKF/performance_ckf.root"
if ckf_perf:
outDirName = "Output_CKF"
outputfile = srcDir / outDirName / "performance_ckf.root"
effContName = "particles"
contName = "tracks"
else:
outDirName = "Output_Seeding"
outputfile = srcDir / outDirName / "performance_seeding.root"
effContName = "seeds"
contName = "seeds"

outputDir = Path(srcDir / outDirName)
outputDir.mkdir(exist_ok=True)
run_ckf(params, keys, outputDir)
rootFile = uproot.open(outputfile)
self.res["eff"].append(rootFile["eff_particles"].member("fElements")[0])
self.res["fakerate"].append(rootFile["fakerate_tracks"].member("fElements")[0])
self.res["eff"].append(rootFile["eff_" + effContName].member("fElements")[0])
self.res["fakerate"].append(
rootFile["fakerate_" + contName].member("fElements")[0]
)
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self.res["duplicaterate"].append(
rootFile["duplicaterate_tracks"].member("fElements")[0]
rootFile["duplicaterate_" + contName].member("fElements")[0]
)

timingfile = srcDir / "Output_CKF/timing.tsv"
timingfile = srcDir / outDirName / "timing.tsv"
timing = pd.read_csv(timingfile, sep="\t")
time_ckf = float(
timing[timing["identifier"].str.match("Algorithm:TrackFindingAlgorithm")][
"time_perevent_s"
]
)

if ckf_perf:
time_ckf = float(
timing[
timing["identifier"].str.match("Algorithm:TrackFindingAlgorithm")
]["time_perevent_s"]
)
else:
time_ckf = 0
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time_seeding = float(
timing[timing["identifier"].str.match("Algorithm:SeedingAlgorithm")][
"time_perevent_s"
Expand Down
40 changes: 29 additions & 11 deletions Examples/Scripts/Optimization/Orion_tuning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,6 +83,7 @@ def __call__(
maxPtScattering,
deltaRMin,
deltaRMax,
ckf_perf=True,
):

params = [
Expand All @@ -106,24 +107,41 @@ def __call__(
"deltaRMax",
]

outputDir = Path(srcDir / "Output_CKF")
outputfile = srcDir / "Output_CKF/performance_ckf.root"
if ckf_perf:
outDirName = "Output_CKF"
outputfile = srcDir / outDirName / "performance_ckf.root"
effContName = "particles"
contName = "tracks"
else:
outDirName = "Output_Seeding"
outputfile = srcDir / outDirName / "performance_seeding.root"
effContName = "seeds"
contName = "seeds"

outputDir = Path(srcDir / outDirName)
outputDir.mkdir(exist_ok=True)
run_ckf(params, keys, outputDir)
rootFile = uproot.open(outputfile)
self.res["eff"].append(rootFile["eff_particles"].member("fElements")[0])
self.res["fakerate"].append(rootFile["fakerate_tracks"].member("fElements")[0])
self.res["eff"].append(rootFile["eff_" + effContName].member("fElements")[0])
self.res["fakerate"].append(
rootFile["fakerate_" + contName].member("fElements")[0]
)
self.res["duplicaterate"].append(
rootFile["duplicaterate_tracks"].member("fElements")[0]
rootFile["duplicaterate_" + contName].member("fElements")[0]
)

timingfile = srcDir / "Output_CKF/timing.tsv"
timingfile = srcDir / outDirName / "timing.tsv"
timing = pd.read_csv(timingfile, sep="\t")
time_ckf = float(
timing[timing["identifier"].str.match("Algorithm:TrackFindingAlgorithm")][
"time_perevent_s"
]
)

if ckf_perf:
time_ckf = float(
timing[
timing["identifier"].str.match("Algorithm:TrackFindingAlgorithm")
]["time_perevent_s"]
)
else:
time_ckf = 0

time_seeding = float(
timing[timing["identifier"].str.match("Algorithm:SeedingAlgorithm")][
"time_perevent_s"
Expand Down
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