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# -*- coding: utf-8 -*-
"""
[Martinez-Gil2024] Augmenting the Interpretability of GraphCodeBERT for Code Similarity Tasks, arXiv preprint arXiv:2410.05275, 2024
@author: Jorge Martinez-Gil
"""
import os
from transformers import RobertaTokenizer, RobertaModel
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import itertools
# Initialize GraphCodeBERT
tokenizer = RobertaTokenizer.from_pretrained("microsoft/graphcodebert-base")
model = RobertaModel.from_pretrained("microsoft/graphcodebert-base")
# Define the classical sorting algorithms
sorting_algorithms = {
"Bubble_Sort": """
def bubble_sort(arr):
n = len(arr)
for i in range(n):
for j in range(0, n-i-1):
if arr[j] > arr[j+1]:
arr[j], arr[j+1] = arr[j+1], arr[j]
return arr
""",
"Selection_Sort": """
def selection_sort(arr):
for i in range(len(arr)):
min_idx = i
for j in range(i+1, len(arr)):
if arr[j] < arr[min_idx]:
min_idx = j
arr[i], arr[min_idx] = arr[min_idx], arr[i]
return arr
""",
"Insertion_Sort": """
def insertion_sort(arr):
for i in range(1, len(arr)):
key = arr[i]
j = i-1
while j >=0 and key < arr[j]:
arr[j + 1] = arr[j]
j -= 1
arr[j + 1] = key
return arr
""",
"Merge_Sort": """
def merge_sort(arr):
if len(arr) > 1:
mid = len(arr)//2
L = arr[:mid]
R = arr[mid:]
merge_sort(L)
merge_sort(R)
i = j = k = 0
while i < len(L) and j < len(R):
if L[i] < R[j]:
arr[k] = L[i]
i += 1
else:
arr[k] = R[j]
j += 1
k += 1
while i < len(L):
arr[k] = L[i]
i += 1
k += 1
while j < len(R):
arr[k] = R[j]
j += 1
k += 1
return arr
""",
"Quick_Sort": """
def partition(arr, low, high):
i = (low-1)
pivot = arr[high]
for j in range(low, high):
if arr[j] <= pivot:
i = i+1
arr[i], arr[j] = arr[j], arr[i]
arr[i+1], arr[high] = arr[high], arr[i+1]
return (i+1)
def quick_sort(arr, low, high):
if low < high:
pi = partition(arr, low, high)
quick_sort(arr, low, pi-1)
quick_sort(arr, pi+1, high)
return arr
"""
}
# Function to get token embeddings for a code snippet
def get_token_embeddings(code):
inputs = tokenizer(code, return_tensors="pt", max_length=512, truncation=True, padding=True)
outputs = model(**inputs)
token_embeddings = outputs.last_hidden_state.squeeze().detach().numpy()
tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'].squeeze())
return token_embeddings, tokens
# Directory to save images
output_dir = "pca_pairwise_comparisons"
os.makedirs(output_dir, exist_ok=True)
# Generate all possible pairs of sorting algorithms
algorithm_pairs = list(itertools.combinations(sorting_algorithms.keys(), 2))
# Loop over each pair and generate the visualizations
for (algo1_name, algo2_name) in algorithm_pairs:
algo1_code = sorting_algorithms[algo1_name]
algo2_code = sorting_algorithms[algo2_name]
# Get token embeddings for both algorithms
algo1_embeddings, algo1_tokens = get_token_embeddings(algo1_code)
algo2_embeddings, algo2_tokens = get_token_embeddings(algo2_code)
# Combine embeddings
all_embeddings = np.concatenate((algo1_embeddings, algo2_embeddings), axis=0)
# Reduce dimensionality to 2D using PCA
pca = PCA(n_components=2)
embeddings_2d = pca.fit_transform(all_embeddings)
# Plotting the token embeddings in 2D
plt.figure(figsize=(10, 8), dpi=300)
# Scatter plot for the first algorithm tokens
plt.scatter(embeddings_2d[:len(algo1_tokens), 0],
embeddings_2d[:len(algo1_tokens), 1],
color='red', s=50, label=algo1_name, alpha=0.8)
# Scatter plot for the second algorithm tokens
plt.scatter(embeddings_2d[len(algo1_tokens):, 0],
embeddings_2d[len(algo1_tokens):, 1],
color='blue', s=50, label=algo2_name, alpha=0.8)
# Make the visualization more professional
plt.xticks([])
plt.yticks([])
plt.xlabel('')
plt.ylabel('')
plt.grid(False)
plt.legend()
# Save the figure as a high-quality PNG file
output_file = os.path.join(output_dir, f"{algo1_name}_vs_{algo2_name}_tokens_2d_pca.png")
plt.savefig(output_file, format='png', dpi=300, bbox_inches='tight')
# Show the plot
plt.close()
print("All pairwise comparison images have been generated.")
import gradio as gr
from io import BytesIO
from PIL import Image
def compare_algorithms(algo1_name, algo2_name):
algo1_code = sorting_algorithms[algo1_name]
algo2_code = sorting_algorithms[algo2_name]
# Get token embeddings
algo1_embeddings, algo1_tokens = get_token_embeddings(algo1_code)
algo2_embeddings, algo2_tokens = get_token_embeddings(algo2_code)
# Combine and reduce
all_embeddings = np.concatenate((algo1_embeddings, algo2_embeddings), axis=0)
pca = PCA(n_components=2)
embeddings_2d = pca.fit_transform(all_embeddings)
# Plot
plt.figure(figsize=(6, 5), dpi=150)
plt.scatter(embeddings_2d[:len(algo1_tokens), 0], embeddings_2d[:len(algo1_tokens), 1], color='red', s=20, label=algo1_name)
plt.scatter(embeddings_2d[len(algo1_tokens):, 0], embeddings_2d[len(algo1_tokens):, 1], color='blue', s=20, label=algo2_name)
plt.xticks([]); plt.yticks([]); plt.grid(False); plt.legend()
# Save to BytesIO
buf = BytesIO()
plt.savefig(buf, format='png', bbox_inches='tight')
plt.close()
buf.seek(0)
return Image.open(buf)
interface = gr.Interface(
fn=compare_algorithms,
inputs=[
gr.Dropdown(choices=list(sorting_algorithms.keys()), label="Algorithm 1"),
gr.Dropdown(choices=list(sorting_algorithms.keys()), label="Algorithm 2")
],
outputs=gr.Image(type="pil", label="Token PCA Plot"),
title="Code Similarity Visualization with GraphCodeBERT"
)
if __name__ == "__main__":
interface.launch()
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