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import math
from datetime import datetime
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
log_files = [
'call_history_sentiment_1_bash.csv',
'call_history_text2int_1_bash.csv',
]
for log_file in log_files:
path_ = f"./data/{log_file}"
df = pd.read_csv(filepath_or_buffer=path_, sep=";")
df["finished_ts"] = df["finished"].apply(
lambda x: datetime.strptime(x, "%Y-%m-%d %H:%M:%S.%f").timestamp())
df["started_ts"] = df["started"].apply(
lambda x: datetime.strptime(x, "%Y-%m-%d %H:%M:%S.%f").timestamp())
df["elapsed"] = df["finished_ts"] - df["started_ts"]
df["success"] = df["outputs"].apply(lambda x: 0 if "Time-out" in x else 1)
student_numbers = sorted(df['active_students'].unique())
bins_dict = dict() # bins size for each group
min_finished_dict = dict() # zero time for each group
for student_number in student_numbers:
# for each student group calculates bins size and zero time
min_finished = df["finished_ts"][df["active_students"] == student_number].min()
max_finished = df["finished_ts"][df["active_students"] == student_number].max()
bins = math.ceil(max_finished - min_finished)
bins_dict.update({student_number: bins})
min_finished_dict.update({student_number: min_finished})
print(f"student number: {student_number}")
print(f"min finished: {min_finished}")
print(f"max finished: {max_finished}")
print(f"bins finished seconds: {bins}, minutes: {bins / 60}")
df["time_line"] = None
for student_number in student_numbers:
# calculates time-line for each student group
df["time_line"] = df.apply(
lambda x: x["finished_ts"] - min_finished_dict[student_number]
if x["active_students"] == student_number
else x["time_line"],
axis=1
)
# creates a '.csv' from the dataframe
df.to_csv(f"./data/processed_{log_file}", index=False, sep=";")
result = df.groupby(['active_students', 'success']) \
.agg({
'elapsed': ['mean', 'median', 'min', 'max'],
'success': ['count'],
})
print(f"Results for {log_file}")
print(result, "\n")
title = None
if "sentiment" in log_file.lower():
title = "API result for 'sentiment-analysis' endpoint"
elif "text2int" in log_file.lower():
title = "API result for 'text2int' endpoint"
for student_number in student_numbers:
# Prints percentage of the successful and failed calls
try:
failed_calls = result.loc[(student_number, 0), 'success'][0]
except:
failed_calls = 0
successful_calls = result.loc[(student_number, 1), 'success'][0]
percentage = (successful_calls / (failed_calls + successful_calls)) * 100
print(f"Percentage of successful API calls for {student_number} students: {percentage.__round__(2)}")
rows = len(student_numbers)
fig, axs = plt.subplots(rows, 2) # (rows, columns)
for index, student_number in enumerate(student_numbers):
# creates a boxplot for each test group
data = df[df["active_students"] == student_number]
axs[index][0].boxplot(x=data["elapsed"]) # axs[row][column]
# axs[index][0].set_title(f'Boxplot for {student_number} students')
axs[index][0].set_xlabel(f'student number {student_number}')
axs[index][0].set_ylabel('Elapsed time (s)')
# creates a histogram for each test group
axs[index][1].hist(x=data["elapsed"], bins=25) # axs[row][column]
# axs[index][1].set_title(f'Histogram for {student_number} students')
axs[index][1].set_xlabel('seconds')
axs[index][1].set_ylabel('Count of API calls')
fig.suptitle(title, fontsize=16)
fig, axs = plt.subplots(rows, 1) # (rows, columns)
for index, student_number in enumerate(student_numbers):
# creates a histogram and shows API calls on a timeline for each test group
data = df[df["active_students"] == student_number]
print(data["time_line"].head(10))
axs[index].hist(x=data["time_line"], bins=bins_dict[student_number]) # axs[row][column]
# axs[index][1].set_title(f'Histogram for {student_number} students')
axs[index].set_xlabel('seconds')
axs[index].set_ylabel('Count of API calls')
fig.suptitle(title, fontsize=16)
plt.show()
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