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"""
Data processing utilities for the Coding Expert model
"""
import json
import os
from pathlib import Path
import jsonlines
from typing import Dict, List, Any, Optional, Tuple
import hashlib
import datetime
import logging
import numpy as np
import pandas as pd
from datasets import Dataset
from tqdm import tqdm
import ast
import re
from collections import Counter
class CodeDataProcessor:
def __init__(self, output_dir: str = "processed_data"):
self.output_dir = Path(output_dir)
self.output_dir.mkdir(exist_ok=True)
self.logger = self._setup_logger()
def _setup_logger(self) -> logging.Logger:
"""Setup logging specific to code processing"""
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
return logger
def process_code(self, code: str, language: str = "python") -> Dict[str, Any]:
"""Process and analyze code snippet"""
try:
# Basic cleaning
code = self._clean_code(code)
# Parse AST if possible
ast_info = self._parse_ast(code, language)
# Extract code metrics
metrics = self._extract_code_metrics(code, ast_info)
# Identify patterns and anti-patterns
patterns = self._identify_patterns(code)
return {
"code": code,
"language": language,
"ast_info": ast_info,
"metrics": metrics,
"patterns": patterns
}
except Exception as e:
self.logger.warning(f"Error processing code: {str(e)}")
return {"error": str(e)}
def _clean_code(self, code: str) -> str:
"""Clean code by removing unnecessary whitespace and comments"""
# Remove trailing whitespace
code = code.strip()
# Remove empty lines
lines = [line.strip() for line in code.split('\n') if line.strip()]
code = '\n'.join(lines)
return code
def _parse_ast(self, code: str, language: str) -> Dict[str, Any]:
"""Parse code into AST and extract structure"""
try:
if language == "python":
tree = ast.parse(code)
return {
"num_functions": len([node for node in ast.walk(tree) if isinstance(node, ast.FunctionDef)]),
"num_classes": len([node for node in ast.walk(tree) if isinstance(node, ast.ClassDef)]),
"complexity": self._calculate_complexity(tree)
}
return {}
except Exception as e:
return {"error": str(e)}
def _calculate_complexity(self, tree: ast.AST) -> int:
"""Calculate cyclomatic complexity"""
complexity = 1 # Start with 1 for the main program
for node in ast.walk(tree):
if isinstance(node, (ast.If, ast.For, ast.While, ast.Try, ast.ExceptHandler)):
complexity += 1
return complexity
def _extract_code_metrics(self, code: str, ast_info: Dict[str, Any]) -> Dict[str, Any]:
"""Extract various code metrics"""
metrics = {
"length": len(code),
"lines": len(code.split('\n')),
"tokens": len(code.split()),
"unique_tokens": len(set(code.split())),
"ast_complexity": ast_info.get("complexity", 0),
"function_count": ast_info.get("num_functions", 0),
"class_count": ast_info.get("num_classes", 0)
}
# Calculate token distribution
tokens = code.split()
token_dist = Counter(tokens)
metrics["token_distribution"] = token_dist.most_common(5)
return metrics
def _identify_patterns(self, code: str) -> Dict[str, List[str]]:
"""Identify common code patterns and anti-patterns"""
patterns = {
"design_patterns": [],
"anti_patterns": [],
"security_issues": []
}
# Look for common design patterns
if "class" in code and "def" in code:
patterns["design_patterns"].append("Class-based design")
# Look for anti-patterns
if "global" in code:
patterns["anti_patterns"].append("Global variables")
# Look for security issues
if "eval(" in code:
patterns["security_issues"].append("Eval usage")
return patterns
def process_dataset(self, dataset: Dataset, dataset_name: str) -> List[Dict[str, Any]]:
"""Process a complete dataset"""
processed = []
error_count = 0
self.logger.info(f"Processing {dataset_name} dataset with {len(dataset)} samples")
for idx, example in enumerate(tqdm(dataset, desc=f"Processing {dataset_name}")):
try:
processed_example = self._process_example(example, dataset_name)
processed.append(processed_example)
except Exception as e:
error_count += 1
self.logger.error(f"Error processing example {idx} in {dataset_name}: {str(e)}")
self.logger.info(f"Processed {len(processed)} examples")
self.logger.info(f"Encountered {error_count} errors")
return processed
def _process_example(self, example: Dict[str, Any], dataset_name: str) -> Dict[str, Any]:
"""Process a single example based on dataset type"""
if dataset_name == "CodeSearchNet":
return self._process_code_search_net(example)
elif dataset_name == "HumanEval":
return self._process_human_eval(example)
elif dataset_name == "MBPP":
return self._process_mbpp(example)
elif dataset_name == "Spider":
return self._process_spider(example)
elif dataset_name == "DeepFix":
return self._process_deep_fix(example)
elif dataset_name == "CodeXGLUE":
return self._process_codexglue(example)
else:
raise ValueError(f"Unknown dataset: {dataset_name}")
def _process_code_search_net(self, example: Dict[str, Any]) -> Dict[str, Any]:
"""Process CodeSearchNet example"""
return {
"code": example["code"].strip(),
"docstring": example["docstring"].strip(),
"language": example["language"],
"function_name": example["function_name"],
"code_analysis": self.process_code(example["code"]) # Reuse code processing
}
def _process_human_eval(self, example: Dict[str, Any]) -> Dict[str, Any]:
"""Process HumanEval example"""
return {
"task_id": example["task_id"],
"prompt": example["prompt"].strip(),
"solution": example["canonical_solution"].strip(),
"test": example["test"].strip(),
"entry_point": example["entry_point"],
"code_analysis": self.process_code(example["canonical_solution"]) # Reuse code processing
}
def _process_mbpp(self, example: Dict[str, Any]) -> Dict[str, Any]:
"""Process MBPP example"""
return {
"task_id": example["task_id"],
"problem": example["text"].strip(),
"solution": example["code"].strip(),
"test_list": example["test_list"],
"challenge_test_list": example["challenge_test_list"],
"code_analysis": self.process_code(example["code"]) # Reuse code processing
}
def _process_spider(self, example: Dict[str, Any]) -> Dict[str, Any]:
"""Process Spider example"""
return {
"query": example["query"].strip(),
"question": example["question"].strip(),
"db_id": example["db_id"],
"sql": example["sql"].strip(),
"code_analysis": self.process_code(example["sql"]) # Reuse code processing
}
def _process_deep_fix(self, example: Dict[str, Any]) -> Dict[str, Any]:
"""Process DeepFix example"""
return {
"original_code": example["code"].strip(),
"fixed_code": example["fixed_code"].strip(),
"error_type": example["error_type"],
"code_analysis": self.process_code(example["fixed_code"]) # Reuse code processing
}
def _process_codexglue(self, example: Dict[str, Any]) -> Dict[str, Any]:
"""Process CodeXGLUE example"""
return {
"code": example["code"].strip(),
"docstring": example["docstring"].strip(),
"task": example["task"],
"language": example["language"],
"code_analysis": self.process_code(example["code"]) # Reuse code processing
}
def save_to_jsonl(self, data: List[Dict[str, Any]], filename: str) -> Path:
"""Save processed data to JSONL file"""
filepath = self.output_dir / filename
with jsonlines.open(filepath, mode='w') as writer:
writer.write_all(data)
self.logger.info(f"Saved data to {filepath}")
return filepath
def print_sample(self, data: List[Dict[str, Any]], count: int = 3):
"""Print sample of processed data"""
self.logger.info("\nSample data:")
for i, example in enumerate(data[:count]):
self.logger.info(f"\nSample {i+1}:")
self.logger.info(json.dumps(example, indent=2))
def print_memory_usage(self):
"""Print current memory usage"""
process = psutil.Process()
memory_info = process.memory_info()
self.logger.info(f"Current memory usage: {memory_info.rss / 1024 / 1024:.2f} MB")
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