From 3ed70c99faf3437e87ea676c9cf257cc2c7904a8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?S=C3=B6ren=20Henning?= <soeren.henning@email.uni-kiel.de>
Date: Wed, 3 Nov 2021 14:02:54 +0100
Subject: [PATCH] Minor code cleanup
---
analysis/src/demand.py | 11 +++--------
1 file changed, 3 insertions(+), 8 deletions(-)
diff --git a/analysis/src/demand.py b/analysis/src/demand.py
index f58b7b2df..2178ab7c5 100644
--- a/analysis/src/demand.py
+++ b/analysis/src/demand.py
@@ -7,10 +7,9 @@ from sklearn.linear_model import LinearRegression
def demand(exp_id, directory, threshold, warmup_sec):
raw_runs = []
- # Compute SL, i.e., lag trend, for each tested configuration
+ # Compute SLI, i.e., lag trend, for each tested configuration
filenames = [filename for filename in os.listdir(directory) if filename.startswith(f"exp{exp_id}") and "lag-trend" in filename and filename.endswith(".csv")]
for filename in filenames:
- #print(filename)
run_params = filename[:-4].split("_")
dim_value = run_params[1]
instances = run_params[2]
@@ -30,24 +29,20 @@ def demand(exp_id, directory, threshold, warmup_sec):
Y_pred = linear_regressor.predict(X) # make predictions
trend_slope = linear_regressor.coef_[0][0]
- #print(linear_regressor.coef_)
row = {'load': int(dim_value), 'resources': int(instances), 'trend_slope': trend_slope}
- #print(row)
raw_runs.append(row)
runs = pd.DataFrame(raw_runs)
# Group by the load and resources to handle repetitions, and take from the reptitions the median
- # for even reptitions the the average of the two middle values is used
+ # for even reptitions, the mean of the two middle values is used
medians = runs.groupby(by=['load', 'resources'], as_index=False).median()
- # Set suitable = True if SLOs are met, i.e., lag trend is below threshold_ratio
- # Calculate the absolute threshold for each row based on threshold_ratio and check if lag is below this threshold
+ # Set suitable = True if SLOs are met, i.e., lag trend slope is below threshold
medians["suitable"] = medians.apply(lambda row: row['trend_slope'] < threshold, axis=1)
suitable = medians[medians.apply(lambda x: x['suitable'], axis=1)]
- #print(suitable)
# Compute minimal demand per load intensity
demand_per_load = suitable.groupby(by=['load'], as_index=False)['resources'].min()
--
GitLab