This revision was automatically updated to reflect the committed changes.
Closed by commit rC344990: [analyzer] [testing] Compute data on path length,
compute percentiles (authored by george.karpenkov, committed by ).
Herald added a subscriber: cfe-commits.
Changed prior to commit:
https://reviews.llvm.org/D52844?vs=168160&id=170556#toc
Repository:
rC Clang
https://reviews.llvm.org/D52844
Files:
utils/analyzer/CmpRuns.py
Index: utils/analyzer/CmpRuns.py
===================================================================
--- utils/analyzer/CmpRuns.py
+++ utils/analyzer/CmpRuns.py
@@ -281,9 +281,21 @@
return res
+def computePercentile(l, percentile):
+ """
+ Return computed percentile.
+ """
+ return sorted(l)[int(round(percentile * len(l) + 0.5)) - 1]
+
def deriveStats(results):
# Assume all keys are the same in each statistics bucket.
combined_data = defaultdict(list)
+
+ # Collect data on paths length.
+ for report in results.reports:
+ for diagnostic in report.diagnostics:
+ combined_data['PathsLength'].append(diagnostic.getPathLength())
+
for stat in results.stats:
for key, value in stat.iteritems():
combined_data[key].append(value)
@@ -293,6 +305,8 @@
"max": max(values),
"min": min(values),
"mean": sum(values) / len(values),
+ "90th %tile": computePercentile(values, 0.9),
+ "95th %tile": computePercentile(values, 0.95),
"median": sorted(values)[len(values) / 2],
"total": sum(values)
}
Index: utils/analyzer/CmpRuns.py
===================================================================
--- utils/analyzer/CmpRuns.py
+++ utils/analyzer/CmpRuns.py
@@ -281,9 +281,21 @@
return res
+def computePercentile(l, percentile):
+ """
+ Return computed percentile.
+ """
+ return sorted(l)[int(round(percentile * len(l) + 0.5)) - 1]
+
def deriveStats(results):
# Assume all keys are the same in each statistics bucket.
combined_data = defaultdict(list)
+
+ # Collect data on paths length.
+ for report in results.reports:
+ for diagnostic in report.diagnostics:
+ combined_data['PathsLength'].append(diagnostic.getPathLength())
+
for stat in results.stats:
for key, value in stat.iteritems():
combined_data[key].append(value)
@@ -293,6 +305,8 @@
"max": max(values),
"min": min(values),
"mean": sum(values) / len(values),
+ "90th %tile": computePercentile(values, 0.9),
+ "95th %tile": computePercentile(values, 0.95),
"median": sorted(values)[len(values) / 2],
"total": sum(values)
}
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