55 lines
2.3 KiB
Diff
55 lines
2.3 KiB
Diff
|
From 2cc1896225cec667893068a3be5d8e7d09043af5 Mon Sep 17 00:00:00 2001
|
||
|
|
From: Pshysimon <caixiaomeng2@huawei.com>
|
||
|
|
Date: Sat, 12 Apr 2025 16:44:11 +0800
|
||
|
|
Subject: [PATCH] fix atune-adm analyse failed problem
|
||
|
|
|
||
|
|
---
|
||
|
|
analysis/optimizer/app_characterization.py | 16 ++++++++--------
|
||
|
|
1 file changed, 8 insertions(+), 8 deletions(-)
|
||
|
|
|
||
|
|
diff --git a/analysis/optimizer/app_characterization.py b/analysis/optimizer/app_characterization.py
|
||
|
|
index 80b6752..738320f 100644
|
||
|
|
--- a/analysis/optimizer/app_characterization.py
|
||
|
|
+++ b/analysis/optimizer/app_characterization.py
|
||
|
|
@@ -202,16 +202,9 @@ class AppCharacterization(WorkloadCharacterization):
|
||
|
|
:param feature_selection: whether to perform feature extraction
|
||
|
|
:param consider_perf: whether to consider perf indicators
|
||
|
|
"""
|
||
|
|
- if consider_perf == None:
|
||
|
|
- consider_perf = self.consider_perf
|
||
|
|
-
|
||
|
|
- data_features = self.get_consider_perf(consider_perf)
|
||
|
|
-
|
||
|
|
cpu_exist, mem_exist, net_quality_exist, net_io_exist, disk_io_exist = self.bottleneck.search_bottleneck(data)
|
||
|
|
bottleneck_binary = (int(cpu_exist) << 4) | (int(mem_exist) << 3) | (int(net_quality_exist) << 2) | (int(net_io_exist) << 1) | int(disk_io_exist)
|
||
|
|
|
||
|
|
- data = data[data_features]
|
||
|
|
-
|
||
|
|
tencoder_path = os.path.join(self.model_path, "tencoder.pkl")
|
||
|
|
aencoder_path = os.path.join(self.model_path, "aencoder.pkl")
|
||
|
|
scaler_path = os.path.join(self.model_path, "scaler.pkl")
|
||
|
|
@@ -226,6 +219,13 @@ class AppCharacterization(WorkloadCharacterization):
|
||
|
|
type_model_clf = joblib.load(type_model_path)
|
||
|
|
app_model_clf = joblib.load(app_model_path)
|
||
|
|
|
||
|
|
+ if consider_perf is None:
|
||
|
|
+ consider_perf = (len(self.scaler.mean_) == len(data.columns))
|
||
|
|
+
|
||
|
|
+ data_features = self.get_consider_perf(consider_perf)
|
||
|
|
+
|
||
|
|
+ data = data[data_features]
|
||
|
|
+
|
||
|
|
data = self.scaler.transform(data)
|
||
|
|
|
||
|
|
if feature_selection:
|
||
|
|
@@ -290,4 +290,4 @@ class AppCharacterization(WorkloadCharacterization):
|
||
|
|
confidence = prediction[1] / len(result)
|
||
|
|
if confidence > 0.5:
|
||
|
|
return bottleneck_binary, prediction[0], confidence
|
||
|
|
- return bottleneck_binary, "default", confidence
|
||
|
|
\ No newline at end of file
|
||
|
|
+ return bottleneck_binary, "default", confidence
|
||
|
|
--
|
||
|
|
2.27.0
|
||
|
|
|