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layer1/ALL-merge/classify.py

@@ -1,126 +0,0 @@
-import json
-from openai import OpenAI
-import time
-import os
-from tqdm import tqdm
-import pandas as pd
-import re
-
-API_KEY="sk-oYh3Xrhg8oDY2gW02c966f31C84449Ad86F9Cd9dF6E64a8d"
-BASE_URL="https://vip.apiyi.com/v1"
-MODEL_DEEPSEEK_V3="deepseek-chat"
-CATEGORIES = ['Physics', 'Chemistry', 'Biological', 'Unknown']
-# 加载JSON数据
-def load_data(file_path):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        data = json.load(f)
-    return data
-
-def classify_question(idx, len,question, options):
-    prompt = f"""
-    Please classify the given question into one of these three categories: 'Physics', 'Chemistry', 'Biological' or 'Unknown'.\n
-    Please format your response by wrapping the category name with the tags [CATEGORY] and [\CATEGORY]. For example, your response should look like one of these:\n
-        - [CATEGORY]Physics[\CATEGORY]
-        - [CATEGORY]Chemistry[\CATEGORY]
-        - [CATEGORY]Biological[\CATEGORY]
-        - [CATEGORY]Unknown[\CATEGORY]
-    Question: {question}\n
-    Options: {options}\n
-    
-    """
-    client = OpenAI(api_key = API_KEY,base_url = BASE_URL)
-    # 重试机制
-    max_retries = 3
-    for attempt in range(max_retries):
-        try:
-            response = client.chat.completions.create(
-                model= MODEL_DEEPSEEK_V3,
-                messages=[
-                    {"role": "system", "content": "You are a helpful educational assistant."},
-                    {"role": "user", "content": prompt}
-                ],
-                temperature=0.3, 
-                stream = False,
-            )
-
-            classification = response.choices[0].message.content.strip()
-            extracted_category = string_extraction(idx,len,classification)
-            if extracted_category in CATEGORIES:
-                return extracted_category
-            else:
-                print(f"Invalid category '{extracted_category}' returned. Retrying. {attempt + 1}/{max_retries}")
-                continue
-
-        except Exception as e:
-            print(f"Error on attempt {attempt + 1}/{max_retries}: {e}")
-            if attempt == max_retries - 1:
-                return 'Error'  # 如果达到最大重试次数，返回错误
-            
-            # 在重试之前等待
-            time.sleep(2)
-    
-    return 'Error'
-def string_extraction(idx,len,classification):
-    pattern = r'\[CATEGORY\](.*?)\[\\CATEGORY\]'
-    match = re.search(pattern, classification)
-    print(f"{idx + 1}/{len}: {match.group(1)}")
-    # if match:
-    #     return match.group(1)
-    # else:
-    #     return "Unknown" 
-
-    return match.group(1) if match else 'Unknown'
-
-def main():
-    # 加载数据
-    file_path = '/home/ubuntu/50T/fsy/MatBench/layer1/ALL-merge/merged.json'  # 替换为你的JSON文件路径
-    data = load_data(file_path)
-    data_length = len(data)
-    # 创建结果列表
-    results = []
-    
-    # 处理每个问题
-    for i, item in enumerate(tqdm(data, desc="Classifying questions")):
-        question = item.get('question', '')
-        text = item['choices']['text']
-        label = item['choices']['label']
-        formatted_choices = " ".join([f"({lbl}) {txt}" for lbl, txt in zip(label, text)])
-        # correct_answer = item.get('correct_answer', '')
-        
-        classification = classify_question(i, data_length,question,formatted_choices)
-        # 添加分类结果
-        item_with_classification = item.copy()
-        item_with_classification['subject_category'] = classification
-        results.append(item_with_classification)
-        
-        # 每处理100个问题，保存一次中间结果
-        if (i + 1) % 100 == 0:
-            # with open('interim_results.json', 'w', encoding='utf-8') as f:
-            #     json.dump(results, f, ensure_ascii=False, indent=4)
-            
-            # 可选：分析中间结果
-            categories = {'Physics': 0, 'Chemistry': 0, 'Biological': 0, 'Unknown': 0}
-            for item in results:
-                categories[item.get('subject_category', 'Unknown')] += 1
-            
-            print(f"Processed {i+1} questions. Current distribution:")
-            for category, count in categories.items():
-                print(f"{category}: {count}")
-                
-        # API速率限制处理
-        time.sleep(0.5)
-    
-    # 保存最终结果
-    with open('/home/ubuntu/50T/fsy/MatBench/layer1/ALL-merge/merged_classified.json', 'w', encoding='utf-8') as f:
-        json.dump(results, f, ensure_ascii=False, indent=4)
-    
-    # 分析结果
-    df = pd.DataFrame(results)
-    category_counts = df['subject_category'].value_counts()
-    print("\nFinal distribution of questions by category:")
-    print(category_counts)
-    
-    print("\nTask completed. Results saved to 'classified_questions.json'")
-
-if __name__ == "__main__":
-    main()

layer1/ALL-merge/classify_muti.py

@@ -1,157 +0,0 @@
-import json
-from openai import OpenAI
-import time
-import os
-from tqdm import tqdm
-import pandas as pd
-import re
-import concurrent.futures
-import threading
-
-API_KEY="sk-oYh3Xrhg8oDY2gW02c966f31C84449Ad86F9Cd9dF6E64a8d"
-BASE_URL="https://vip.apiyi.com/v1"
-MODEL_DEEPSEEK_V3 = "deepseek-chat"
-CATEGORIES = ['Physics', 'Chemistry', 'Biological', 'Unknown']
-
-# Thread-local storage for OpenAI clients
-local = threading.local()
-
-# Lock for thread-safe operations
-write_lock = threading.Lock()
-progress_lock = threading.Lock()
-processed_count = 0
-category_counts = {'Physics': 0, 'Chemistry': 0, 'Biological': 0, 'Unknown': 0}
-
-# 加载JSON数据
-def load_data(file_path):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        data = json.load(f)
-    return data
-
-def get_client():
-    """Get thread-local OpenAI client"""
-    if not hasattr(local, 'client'):
-        local.client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
-    return local.client
-
-def classify_question(idx, total_len, question, options):
-    prompt = f"""
-    Please classify the given question into one of these three categories: 'Physics', 'Chemistry', 'Biological' or 'Unknown'.\n
-    Please format your response by wrapping the category name with the tags [CATEGORY] and [/CATEGORY]. For example, your response should look like one of these:\n
-        - [CATEGORY]Physics[/CATEGORY]
-        - [CATEGORY]Chemistry[/CATEGORY]
-        - [CATEGORY]Biological[/CATEGORY]
-        - [CATEGORY]Unknown[/CATEGORY]
-    Question: {question}\n
-    Options: {options}\n
-    """
-    
-    client = get_client()
-    # 重试机制
-    max_retries = 3
-    for attempt in range(max_retries):
-        try:
-            response = client.chat.completions.create(
-                model=MODEL_DEEPSEEK_V3,
-                messages=[
-                    {"role": "system", "content": "You are a helpful educational assistant."},
-                    {"role": "user", "content": prompt}
-                ],
-                temperature=0.3, 
-                stream=False,
-            )
-            classification = response.choices[0].message.content.strip()
-            extracted_category = string_extraction(idx, total_len, classification)
-            if extracted_category in CATEGORIES:
-                return extracted_category
-            else:
-                with progress_lock:
-                    print(f"Invalid category '{extracted_category}' returned. Retrying. {attempt + 1}/{max_retries}")
-                continue
-        except Exception as e:
-            with progress_lock:
-                print(f"Error on attempt {attempt + 1}/{max_retries}: {e}")
-            if attempt == max_retries - 1:
-                return 'Error'  # 如果达到最大重试次数，返回错误
-            
-            # 在重试之前等待
-            time.sleep(2)
-    
-    return 'Error'
-
-def string_extraction(idx, total_len, classification):
-    pattern = r'\[CATEGORY\](.*?)\[\/CATEGORY\]'
-    match = re.search(pattern, classification)
-    extracted = match.group(1) if match else 'Unknown'
-    
-    with progress_lock:
-        print(f"{idx + 1}/{total_len}: {extracted}")
-    
-    return extracted
-
-def process_item(args):
-    idx, total_len, item = args
-    question = item.get('question', '')
-    text = item['choices']['text']
-    label = item['choices']['label']
-    formatted_choices = " ".join([f"({lbl}) {txt}" for lbl, txt in zip(label, text)])
-    
-    classification = classify_question(idx, total_len, question, formatted_choices)
-    
-    # 添加分类结果
-    item_with_classification = item.copy()
-    item_with_classification['subject_category'] = classification
-    
-    # Update global counters
-    global processed_count
-    with write_lock:
-        processed_count += 1
-        category_counts[classification] += 1
-        
-        # 每处理100个问题，打印一次中间结果
-        if processed_count % 100 == 0:
-            print(f"\nProcessed {processed_count} questions. Current distribution:")
-            for category, count in category_counts.items():
-                print(f"{category}: {count}")
-    
-    # API速率限制处理 - 减少sleep时间，因为多线程已经提供了自然的延迟
-    time.sleep(0.1)
-    
-    return item_with_classification
-
-def main():
-    # 加载数据
-    file_path = '/home/ubuntu/50T/fsy/MatBench/layer1/ALL-merge/merged.json'
-    data = load_data(file_path)
-    data_length = len(data)
-    
-    results = []
-    
-    # 创建参数列表
-    args_list = [(i, data_length, item) for i, item in enumerate(data)]
-    
-    # 设定线程数，根据实际API限制和服务器性能调整
-    num_threads = 10  # 根据需要调整线程数
-    
-    print(f"Starting classification with {num_threads} threads...")
-    
-    # 使用ThreadPoolExecutor进行并行处理
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
-        # 使用tqdm来显示进度
-        futures = list(tqdm(executor.map(process_item, args_list), total=data_length, desc="Classifying questions"))
-        results = futures
-    
-    # 保存最终结果
-    with open('/home/ubuntu/50T/fsy/MatBench/layer1/ALL-merge/merged_classified.json', 'w', encoding='utf-8') as f:
-        json.dump(results, f, ensure_ascii=False, indent=4)
-    
-    # 分析结果
-    df = pd.DataFrame(results)
-    category_counts_final = df['subject_category'].value_counts()
-    print("\nFinal distribution of questions by category:")
-    print(category_counts_final)
-    
-    print("\nTask completed. Results saved to '/home/ubuntu/50T/fsy/MatBench/layer1/ALL-merge/merged_classified.json'")
-
-if __name__ == "__main__":
-    main()

layer2/PGEE/code/classify_muti.py

@@ -1,169 +0,0 @@
-import json
-from openai import OpenAI
-import time
-import os
-from tqdm import tqdm
-import pandas as pd
-import re
-import concurrent.futures
-import threading
-
-API_KEY="sk-oYh3Xrhg8oDY2gW02c966f31C84449Ad86F9Cd9dF6E64a8d"
-BASE_URL="https://vip.apiyi.com/v1"
-MODEL_DEEPSEEK_V3 = "deepseek-chat"
-CATEGORIES = ['Atomic Structure and Interatomic Bonding', 'The Structure of Solids', 'Imperfections in Solids', 'Mechanical Properties of Metals','Dislocations and Strengthening Mechanisms','Failure','Phase Transformations: Development of Microstructure and Alteration of Mechanical Properties','Applications and Processing of Materials','Corrosion and Degradation of Materials','Functional Properties of Materials','Unknown']
-FILE_PATH = '/home/ubuntu/50T/fsy/A/MatBench/layer2/PGEE/code/stepz_final_choice_questions_filtered_only_hard.json'
-OUTPUT_PATH='/home/ubuntu/50T/fsy/A/MatBench/layer2/PGEE/code/stepz_classified_only_hard.json'
-# Thread-local storage for OpenAI clients
-local = threading.local()
-
-# Lock for thread-safe operations
-write_lock = threading.Lock()
-progress_lock = threading.Lock()
-processed_count = 0
-category_counts = {'Atomic Structure and Interatomic Bonding': 0, 'The Structure of Solids': 0, 'Imperfections in Solids': 0, 'Mechanical Properties of Metals':0,'Dislocations and Strengthening Mechanisms':0,'Failure':0,'Phase Transformations: Development of Microstructure and Alteration of Mechanical Properties':0,'Applications and Processing of Materials':0,'Corrosion and Degradation of Materials':0,'Functional Properties of Materials':0,'Unknown':0}
-
-# 加载JSON数据
-def load_data(file_path):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        data = json.load(f)
-    return data
-
-def get_client():
-    """Get thread-local OpenAI client"""
-    if not hasattr(local, 'client'):
-        local.client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
-    return local.client
-
-def classify_question(idx, total_len, question, answer):
-    prompt = f"""
-    Given a question and its answer from the field of Materials Science fundamentals, identify which chapter or category of Materials Science the question belongs to. Choose from the following 10 categories:
-
-    -- Atomic Structure and Interatomic Bonding
-    -- The Structure of Solids
-    -- Imperfections in Solids
-    -- Mechanical Properties of Metals
-    -- Dislocations and Strengthening Mechanisms
-    -- Failure
-    -- Phase Transformations: Development of Microstructure and Alteration of Mechanical Properties
-    -- Applications and Processing of Materials
-    -- Corrosion and Degradation of Materials
-    -- Functional Properties of Materials
-
-    QUESTIONS:{question}\n
-    ANSWER:{answer}\n
-
-    Provide your response by enclosing the category number and name within [CATEGORY] and [/CATEGORY] tags. For example: [CATEGORY]Atomic Structure and Interatomic Bonding[/CATEGORY]
-
-    Analyze both the question and answer carefully to determine the most appropriate category based on the question and options.
-    """
-    
-    client = get_client()
-    # 重试机制
-    max_retries = 3
-    for attempt in range(max_retries):
-        try:
-            response = client.chat.completions.create(
-                model=MODEL_DEEPSEEK_V3,
-                messages=[
-                    {"role": "system", "content": "You are a helpful educational assistant."},
-                    {"role": "user", "content": prompt}
-                ],
-                temperature=0.3, 
-                stream=False,
-            )
-            classification = response.choices[0].message.content.strip()
-            extracted_category = string_extraction(idx, total_len, classification)
-            if extracted_category in CATEGORIES:
-                return extracted_category
-            else:
-                with progress_lock:
-                    print(f"Invalid category '{extracted_category}' returned. Retrying. {attempt + 1}/{max_retries}")
-                continue
-        except Exception as e:
-            with progress_lock:
-                print(f"Error on attempt {attempt + 1}/{max_retries}: {e}")
-            if attempt == max_retries - 1:
-                return 'Error'  # 如果达到最大重试次数，返回错误
-            
-            # 在重试之前等待
-            time.sleep(2)
-    
-    return 'Error'
-
-def string_extraction(idx, total_len, classification):
-    pattern = r'\[CATEGORY\](.*?)\[\/CATEGORY\]'
-    match = re.search(pattern, classification)
-    extracted = match.group(1) if match else 'Unknown'
-    
-    with progress_lock:
-        print(f"{idx + 1}/{total_len}: {extracted}")
-    
-    return extracted
-
-def process_item(args):
-    idx, total_len, item = args
-    question = item.get('question', '')
-    text = item['choices']['text']
-    label = item['choices']['label']
-    # answer = item.get('correct_option','')
-    formatted_choices = " ".join([f"({lbl}) {txt}" for lbl, txt in zip(label, text)])
-    
-    classification = classify_question(idx, total_len, question, formatted_choices)
-    
-    # 添加分类结果
-    item_with_classification = item.copy()
-    item_with_classification['subject_category'] = classification
-    
-    # Update global counters
-    global processed_count
-    with write_lock:
-        processed_count += 1
-        category_counts[classification] += 1
-        
-        # 每处理100个问题，打印一次中间结果
-        if processed_count % 100 == 0:
-            print(f"\nProcessed {processed_count} questions. Current distribution:")
-            for category, count in category_counts.items():
-                print(f"{category}: {count}")
-    
-    # API速率限制处理 - 减少sleep时间，因为多线程已经提供了自然的延迟
-    time.sleep(0.1)
-    
-    return item_with_classification
-
-def main():
-    # 加载数据
-    data = load_data(FILE_PATH)
-    data_length = len(data)
-    
-    results = []
-    
-    # 创建参数列表
-    args_list = [(i, data_length, item) for i, item in enumerate(data)]
-    
-    # 设定线程数，根据实际API限制和服务器性能调整
-    num_threads = 20  # 根据需要调整线程数
-    
-    print(f"Starting classification with {num_threads} threads...")
-    
-    # 使用ThreadPoolExecutor进行并行处理
-    with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
-        # 使用tqdm来显示进度
-        futures = list(tqdm(executor.map(process_item, args_list), total=data_length, desc="Classifying questions"))
-        results = futures
-    
-    # 保存最终结果
-    with open(OUTPUT_PATH, 'w', encoding='utf-8') as f:
-        json.dump(results, f, ensure_ascii=False, indent=4)
-    
-    # 分析结果
-    df = pd.DataFrame(results)
-    category_counts_final = df['subject_category'].value_counts()
-    print("\nFinal distribution of questions by category:")
-    print(category_counts_final)
-    
-    print(f"\nTask completed. Results saved to {OUTPUT_PATH}")
-
-if __name__ == "__main__":
-    main()