From 2cc1896225cec667893068a3be5d8e7d09043af5 Mon Sep 17 00:00:00 2001 From: Pshysimon 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