mirror of
https://github.com/Lore09/Tesi-Magistrale.git
synced 2025-12-19 04:14:35 +00:00
88 lines
3.1 KiB
Python
88 lines
3.1 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 the file
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def read_metrics(file_path):
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with open(file_path, 'r') as file:
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data = yaml.safe_load(file)
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return data['runs']
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# Read metrics from the file
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file_path = 'project/metrics.yaml' # Replace with your file path
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runs = read_metrics(file_path)
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#Flatten data into a list of dictionaries
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data = []
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for run in runs:
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if 'build' in run:
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data.append({'Task': run['n_task'], 'Type': 'Build Time', 'Time': float(run['build']['components_build_time'])})
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if 'code_gen' in run:
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data.append({'Task': run['n_task'], 'Type': 'Generation Time', 'Time': float(run['code_gen']['gen_time'])})
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if 'deploy' in run:
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data.append({'Task': run['n_task'], 'Type': 'Deployment Time', 'Time': float(run['deploy']['components_deploy_time'])})
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# Convert to DataFrame
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df = pd.DataFrame(data)
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# Make sure the 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, color):
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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', data=subset, color=color, showfliers=False)
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# Label median values
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medians = subset.groupby('Task')['Time'].median()
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for i, task in enumerate(medians.index):
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median_value = medians[task]
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plt.text(i, median_value, f'{median_value:.3f}', ha='center', va='center', fontsize=10, color='white',
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bbox=dict(facecolor='black', alpha=0.6, boxstyle='round,pad=0.3'))
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plt.ylabel('Time (seconds)')
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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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# Function to plot line plot with confidence intervals
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def plot_lineplot(metric, filename, color):
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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', data=subset, errorbar=('ci', 95), color=color, linewidth=2, marker='o')
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# Fill missing x-axis values
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min_task = subset['Task'].min()
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max_task = subset['Task'].max()
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all_tasks = np.arange(min_task, max_task + 1)
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plt.xticks(all_tasks)
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# Label points
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for i, row in subset.groupby('Task')['Time'].median().reset_index().iterrows():
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plt.text(row['Task'], row['Time'], f'{row["Time"]:.3f}', ha='center', va='bottom', fontsize=10)
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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.tight_layout()
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plt.savefig(f'benchmark/{filename}')
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plt.close()
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# Plot each metric separately
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plot_boxplot('Build Time', 'build_time_boxplot.png', 'skyblue')
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plot_lineplot('Build Time', 'build_time_lineplot.png', 'skyblue')
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plot_boxplot('Generation Time', 'gen_time_boxplot.png', 'lightgreen')
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plot_lineplot('Generation Time', 'gen_time_lineplot.png', 'lightgreen')
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plot_boxplot('Deployment Time', 'deploy_time_boxplot.png', 'salmon')
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plot_lineplot('Deployment Time', 'deploy_time_lineplot.png', 'salmon')
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print('Plots saved successfully in "benchmark" directory!') |