mirror of
https://github.com/Lore09/Tesi-Magistrale.git
synced 2025-12-19 04:14:35 +00:00
99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
import seaborn as sns
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import matplotlib.pyplot as plt
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import yaml
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import pandas as pd
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import numpy as np
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import os
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# Function to read and parse a metric file
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def read_metrics(file_path, label):
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with open(file_path, 'r') as file:
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data = yaml.safe_load(file)
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runs = data['runs']
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extracted_data = []
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for run in runs:
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if 'build' in run:
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extracted_data.append({'Task': run['n_task'], 'Type': 'Build Time', 'Time': float(run['build']['components_build_time']), 'Source': label})
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if 'code_gen' in run:
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extracted_data.append({'Task': run['n_task'], 'Type': 'Generation Time', 'Time': float(run['code_gen']['gen_time']), 'Source': label})
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if 'deploy' in run:
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extracted_data.append({'Task': run['n_task'], 'Type': 'Deployment Time', 'Time': float(run['deploy']['components_deploy_time']), 'Source': label})
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if 'time_total' in run:
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extracted_data.append({'Task': run['n_task'], 'Type': 'Total Time', 'Time': float(run['time_total']), 'Source': label})
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return extracted_data
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# Paths for the two metric files
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file_path_1 = 'res/metrics/metrics-parallel-nats.yaml' # First metrics file
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file_path_2 = 'res/metrics/metrics-sequential.yaml' # Second metrics file
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# Read and combine the data
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data1 = read_metrics(file_path_1, 'Esecuzione Parallelizzata')
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data2 = read_metrics(file_path_2, 'Esecuzione Sequenziale')
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df = pd.DataFrame(data1 + data2)
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# Ensure benchmark directory exists
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os.makedirs('benchmark', exist_ok=True)
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# Function to plot boxplot
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def plot_boxplot(metric, filename):
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subset = df[df['Type'] == metric]
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plt.figure(figsize=(10, 6))
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sns.boxplot(x='Task', y='Time', hue='Source', data=subset, showfliers=False)
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plt.ylabel('Time (seconds)')
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plt.grid(True, linestyle='--', alpha=0.7)
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plt.legend(title='Source')
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plt.tight_layout()
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plt.savefig(f'benchmark/{filename}')
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plt.close()
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# Function to plot line plot with confidence intervals
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def plot_lineplot(metric, filename):
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subset = df[df['Type'] == metric]
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plt.figure(figsize=(10, 6))
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sns.lineplot(x='Task', y='Time', hue='Source', data=subset, errorbar=('ci', 95), linewidth=2, marker='o')
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plt.xlabel('Number of Tasks')
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plt.ylabel('Time (seconds)')
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plt.grid(True, linestyle='--', alpha=0.7)
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plt.legend(title='Source')
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plt.tight_layout()
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plt.savefig(f'benchmark/{filename}')
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plt.close()
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# Function to plot bar plot with confidence intervals
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def plot_barplot(metric, filename):
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subset = df[df['Type'] == metric]
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plt.figure(figsize=(10, 6))
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sns.barplot(x='Task', y='Time', hue='Source', data=subset, errorbar=('ci', 95), palette=['salmon', 'skyblue'])
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plt.xlabel('Task', fontsize=14) # Aumenta la dimensione del font dell'asse X
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plt.ylabel('Time (seconds)', fontsize=14) # Aumenta la dimensione del font dell'asse Y
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plt.xticks(fontsize=12) # Modifica la dimensione del font dei tick dell'asse X
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plt.yticks(fontsize=12) # Modifica la dimensione del font dei tick dell'asse Y
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plt.legend(title='Source', title_fontsize=14, fontsize=12) # Modifica il font della legenda
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plt.grid(True, linestyle='--', alpha=0.7)
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plt.tight_layout()
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plt.savefig(f'benchmark/{filename}')
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plt.close()
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# Generate plots for each metric
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metrics = ['Build Time', 'Generation Time', 'Deployment Time', 'Total Time']
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filenames = ['build_time', 'gen_time', 'deploy_time', 'total_time']
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for metric, filename in zip(metrics, filenames):
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#plot_boxplot(metric, f'{filename}_boxplot.png')
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#plot_lineplot(metric, f'{filename}_lineplot.png')
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plot_barplot(metric, f'{filename}_paired_barplot.png')
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print('Comparison plots saved successfully in "benchmark" directory!')
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