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Commit 0b87377c authored by Sören Henning's avatar Sören Henning
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Clean up

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execution/scalability-graph.ipynb
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%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
print("hello") print("hello")
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
import os import os
import requests import requests
from datetime import datetime, timedelta, timezone from datetime import datetime, timedelta, timezone
import pandas as pd import pandas as pd
from sklearn.linear_model import LinearRegression from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
os.getcwd() os.getcwd()
``` ```
%% Cell type:code id: tags:
```
exp_id = 35
warmup_sec = 60
threshold = 2000 #slope
```
%% Cell type:code id: tags:outputPrepend,outputPrepend %% Cell type:code id: tags:outputPrepend,outputPrepend
``` ```
exp_id = 9 #exp_id = 35
#os.chdir("./results-new") #os.chdir("./results-new")
raw_runs = [] raw_runs = []
filenames = [filename for filename in os.listdir('.') if filename.startswith(f"exp{exp_id}") and filename.endswith(".csv")] filenames = [filename for filename in os.listdir('.') if filename.startswith(f"exp{exp_id}") and filename.endswith(".csv")]
for filename in filenames: for filename in filenames:
#print(filename) #print(filename)
run_params = filename[:-4].split("_") run_params = filename[:-4].split("_")
dim_value = run_params[2] dim_value = run_params[2]
instances = run_params[3] instances = run_params[3]
df = pd.read_csv(filename) df = pd.read_csv(filename)
input = df.loc[df['topic'] == "input"] input = df.loc[df['topic'] == "input"]
input['sec_start'] = input.loc[0:, 'timestamp'] - input.at[0, 'timestamp'] #print(input)
regress = input.loc[input['sec_start'] >= 60] # Warm-Up input['sec_start'] = input.loc[0:, 'timestamp'] - input.iloc[0]['timestamp']
#print(input)
#print(input.iloc[0, 'timestamp'])
regress = input.loc[input['sec_start'] >= warmup_sec] # Warm-Up
#regress = input
#input.plot(kind='line',x='timestamp',y='value',color='red') #input.plot(kind='line',x='timestamp',y='value',color='red')
#plt.show() #plt.show()
X = regress.iloc[:, 2].values.reshape(-1, 1) # values converts it into a numpy array X = regress.iloc[:, 2].values.reshape(-1, 1) # values converts it into a numpy array
Y = regress.iloc[:, 3].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column Y = regress.iloc[:, 3].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column
linear_regressor = LinearRegression() # create object for the class linear_regressor = LinearRegression() # create object for the class
linear_regressor.fit(X, Y) # perform linear regression linear_regressor.fit(X, Y) # perform linear regression
Y_pred = linear_regressor.predict(X) # make predictions Y_pred = linear_regressor.predict(X) # make predictions
trend_slope = linear_regressor.coef_[0][0] trend_slope = linear_regressor.coef_[0][0]
#print(linear_regressor.coef_) #print(linear_regressor.coef_)
row = {'dim_value': int(dim_value), 'instances': int(instances), 'trend_slope': trend_slope} row = {'dim_value': int(dim_value), 'instances': int(instances), 'trend_slope': trend_slope}
#print(row) #print(row)
raw_runs.append(row) raw_runs.append(row)
runs = pd.DataFrame(raw_runs) runs = pd.DataFrame(raw_runs)
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
runs.head() runs.head()
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
threshold = 10 #threshold = 1000
# Set to true if the trend line has a slope less than # Set to true if the trend line has a slope less than
runs["suitable"] = runs.apply(lambda row: row['trend_slope'] < threshold, axis=1) runs["suitable"] = runs.apply(lambda row: row['trend_slope'] < threshold, axis=1)
runs.columns = runs.columns.str.strip() runs.columns = runs.columns.str.strip()
runs.sort_values(by=["dim_value", "instances"]) runs.sort_values(by=["dim_value", "instances"])
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
filtered = runs[runs.apply(lambda x: x['suitable'], axis=1)] filtered = runs[runs.apply(lambda x: x['suitable'], axis=1)]
grouped = filtered.groupby(['dim_value'])['instances'].min() grouped = filtered.groupby(['dim_value'])['instances'].min()
min_suitable_instances = grouped.to_frame().reset_index() min_suitable_instances = grouped.to_frame().reset_index()
min_suitable_instances min_suitable_instances
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
min_suitable_instances.plot(kind='line',x='dim_value',y='instances') min_suitable_instances.plot(kind='line',x='dim_value',y='instances')
plt.show() plt.show()
``` ```
%% Cell type:code id: tags: %% Cell type:code id: tags:
``` ```
``` ```
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