mirror of
https://github.com/Lore09/Tesi-Magistrale.git
synced 2025-12-19 12:24:31 +00:00
99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
import seaborn as sns
|
|
import matplotlib.pyplot as plt
|
|
import yaml
|
|
import pandas as pd
|
|
import numpy as np
|
|
import os
|
|
|
|
# Function to read and parse a metric file
|
|
def read_metrics(file_path, label):
|
|
with open(file_path, 'r') as file:
|
|
data = yaml.safe_load(file)
|
|
runs = data['runs']
|
|
|
|
extracted_data = []
|
|
for run in runs:
|
|
if 'build' in run:
|
|
extracted_data.append({'Task': run['n_task'], 'Type': 'Build Time', 'Time': float(run['build']['components_build_time']), 'Source': label})
|
|
|
|
if 'code_gen' in run:
|
|
extracted_data.append({'Task': run['n_task'], 'Type': 'Generation Time', 'Time': float(run['code_gen']['gen_time']), 'Source': label})
|
|
|
|
if 'deploy' in run:
|
|
extracted_data.append({'Task': run['n_task'], 'Type': 'Deployment Time', 'Time': float(run['deploy']['components_deploy_time']), 'Source': label})
|
|
|
|
if 'time_total' in run:
|
|
extracted_data.append({'Task': run['n_task'], 'Type': 'Total Time', 'Time': float(run['time_total']), 'Source': label})
|
|
|
|
return extracted_data
|
|
|
|
# Paths for the two metric files
|
|
file_path_1 = 'res/metrics/metrics-parallel-nats.yaml' # First metrics file
|
|
file_path_2 = 'res/metrics/metrics-sequential.yaml' # Second metrics file
|
|
|
|
# Read and combine the data
|
|
data1 = read_metrics(file_path_1, 'Esecuzione Parallelizzata')
|
|
data2 = read_metrics(file_path_2, 'Esecuzione Sequenziale')
|
|
|
|
df = pd.DataFrame(data1 + data2)
|
|
|
|
# Ensure benchmark directory exists
|
|
os.makedirs('benchmark', exist_ok=True)
|
|
|
|
# Function to plot boxplot
|
|
def plot_boxplot(metric, filename):
|
|
subset = df[df['Type'] == metric]
|
|
plt.figure(figsize=(10, 6))
|
|
sns.boxplot(x='Task', y='Time', hue='Source', data=subset, showfliers=False)
|
|
|
|
plt.ylabel('Time (seconds)')
|
|
plt.grid(True, linestyle='--', alpha=0.7)
|
|
plt.legend(title='Source')
|
|
plt.tight_layout()
|
|
plt.savefig(f'benchmark/{filename}')
|
|
plt.close()
|
|
|
|
# Function to plot line plot with confidence intervals
|
|
def plot_lineplot(metric, filename):
|
|
subset = df[df['Type'] == metric]
|
|
plt.figure(figsize=(10, 6))
|
|
sns.lineplot(x='Task', y='Time', hue='Source', data=subset, errorbar=('ci', 95), linewidth=2, marker='o')
|
|
|
|
plt.xlabel('Number of Tasks')
|
|
plt.ylabel('Time (seconds)')
|
|
plt.grid(True, linestyle='--', alpha=0.7)
|
|
plt.legend(title='Source')
|
|
plt.tight_layout()
|
|
plt.savefig(f'benchmark/{filename}')
|
|
plt.close()
|
|
|
|
# Function to plot bar plot with confidence intervals
|
|
def plot_barplot(metric, filename):
|
|
subset = df[df['Type'] == metric]
|
|
plt.figure(figsize=(10, 6))
|
|
|
|
sns.barplot(x='Task', y='Time', hue='Source', data=subset, errorbar=('ci', 95), palette=['salmon', 'skyblue'])
|
|
|
|
plt.xlabel('Task', fontsize=14) # Aumenta la dimensione del font dell'asse X
|
|
plt.ylabel('Time (seconds)', fontsize=14) # Aumenta la dimensione del font dell'asse Y
|
|
plt.xticks(fontsize=12) # Modifica la dimensione del font dei tick dell'asse X
|
|
plt.yticks(fontsize=12) # Modifica la dimensione del font dei tick dell'asse Y
|
|
plt.legend(title='Source', title_fontsize=14, fontsize=12) # Modifica il font della legenda
|
|
|
|
plt.grid(True, linestyle='--', alpha=0.7)
|
|
plt.tight_layout()
|
|
plt.savefig(f'benchmark/{filename}')
|
|
plt.close()
|
|
|
|
|
|
# Generate plots for each metric
|
|
metrics = ['Build Time', 'Generation Time', 'Deployment Time', 'Total Time']
|
|
filenames = ['build_time', 'gen_time', 'deploy_time', 'total_time']
|
|
|
|
for metric, filename in zip(metrics, filenames):
|
|
#plot_boxplot(metric, f'{filename}_boxplot.png')
|
|
#plot_lineplot(metric, f'{filename}_lineplot.png')
|
|
plot_barplot(metric, f'{filename}_paired_barplot.png')
|
|
|
|
print('Comparison plots saved successfully in "benchmark" directory!')
|