Add SmartFineTuner and SmartPricer classes for enhanced product pricing
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Mohamed Salah
269
week6/community-contributions/salah/smart_fine_tuner.py
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269
week6/community-contributions/salah/smart_fine_tuner.py
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import sys
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import os
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sys.path.append("../..")
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import json
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import pickle
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import pandas as pd
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import numpy as np
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from openai import OpenAI
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from dotenv import load_dotenv
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from huggingface_hub import login
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from smart_pricer import SmartPricer, ConfidenceAwareTester
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import re
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from typing import List, Dict, Tuple
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import time
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load_dotenv(override=True)
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os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')
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os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN')
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hf_token = os.environ['HF_TOKEN']
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login(hf_token, add_to_git_credential=True)
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class SmartFineTuner:
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def __init__(self, openai_api_key: str = None):
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self.client = OpenAI(api_key=openai_api_key or os.getenv('OPENAI_API_KEY'))
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self.fine_tuned_model_id = None
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self.training_templates = [
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{
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"system": "You are a product pricing expert. Respond only with the price, no explanation.",
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"user": "Estimate the price of this product:\n\n{description}\n\nPrice: $",
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"weight": 0.4
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},
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{
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"system": "You are a retail pricing expert. Consider market positioning and consumer value.",
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"user": "What would this product sell for in the market?\n\n{description}\n\nMarket price: $",
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"weight": 0.3
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},
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{
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"system": "You analyze product features to determine fair pricing.",
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"user": "Based on the features and quality described, estimate the price:\n\n{description}\n\nEstimated price: $",
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"weight": 0.3
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}
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]
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def prepare_enhanced_training_data(self, train_items: List, template_mix: bool = True) -> List[Dict]:
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training_data = []
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for item in train_items:
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description = self._get_clean_description(item)
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if len(description.strip()) < 20:
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continue
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if hasattr(item, 'price'):
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price = item.price
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else:
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price = item.get('price', 0)
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if price <= 0:
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continue
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templates_to_use = self.training_templates if template_mix else [self.training_templates[0]]
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for template in templates_to_use:
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if template_mix and np.random.random() > template['weight']:
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continue
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user_prompt = template['user'].format(description=description)
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messages = [
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{"role": "system", "content": template['system']},
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{"role": "user", "content": user_prompt},
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{"role": "assistant", "content": f"{price:.2f}"}
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]
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training_data.append({"messages": messages})
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return training_data
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def _get_clean_description(self, item) -> str:
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if hasattr(item, 'test_prompt'):
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prompt = item.test_prompt()
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clean = prompt.replace(" to the nearest dollar", "")
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clean = clean.replace("\n\nPrice is $", "")
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clean = re.sub(r'\$\d+\.?\d*', '', clean)
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clean = re.sub(r'\d+\.?\d*\s*dollars?', '', clean)
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return clean.strip()
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else:
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parts = []
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if 'title' in item and item['title']:
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parts.append(f"Title: {item['title']}")
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if 'description' in item and item['description']:
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parts.append(f"Description: {item['description']}")
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if 'features' in item and item['features']:
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parts.append(f"Features: {item['features']}")
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return '\n'.join(parts)
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def create_training_files(self, train_items: List, val_items: List,
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enhanced: bool = True) -> Tuple[str, str]:
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train_data = self.prepare_enhanced_training_data(train_items, template_mix=enhanced)
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val_data = self.prepare_enhanced_training_data(val_items, template_mix=False)
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print(f"Prepared {len(train_data)} training examples")
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print(f"Prepared {len(val_data)} validation examples")
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train_file = "smart_pricer_train.jsonl"
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val_file = "smart_pricer_validation.jsonl"
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with open(train_file, 'w') as f:
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for example in train_data:
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f.write(json.dumps(example) + '\n')
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with open(val_file, 'w') as f:
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for example in val_data:
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f.write(json.dumps(example) + '\n')
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return train_file, val_file
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def start_fine_tuning(self, train_file: str, val_file: str,
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model: str = "gpt-4o-mini-2024-07-18",
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epochs: int = 1) -> str:
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print(f"Starting fine-tuning with enhanced training data...")
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with open(train_file, 'rb') as f:
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train_file_obj = self.client.files.create(file=f, purpose="fine-tune")
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with open(val_file, 'rb') as f:
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val_file_obj = self.client.files.create(file=f, purpose="fine-tune")
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print(f"Uploaded training file: {train_file_obj.id}")
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print(f"Uploaded validation file: {val_file_obj.id}")
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job = self.client.fine_tuning.jobs.create(
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training_file=train_file_obj.id,
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validation_file=val_file_obj.id,
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model=model,
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hyperparameters={"n_epochs": epochs},
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suffix="smart_pricer"
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)
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self.fine_tuned_model_id = job.id
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print(f"Fine-tuning job created: {job.id}")
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return job.id
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def check_job_status(self, job_id: str) -> Dict:
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job = self.client.fine_tuning.jobs.retrieve(job_id)
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return {
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'status': job.status,
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'model': job.fine_tuned_model,
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'created_at': job.created_at,
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'finished_at': job.finished_at
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}
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def evaluate_fine_tuned_model(self, test_data: List, job_id: str) -> Dict:
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job_info = self.check_job_status(job_id)
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if job_info['status'] != 'succeeded':
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print(f"Job not completed yet. Status: {job_info['status']}")
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return {}
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fine_tuned_model = job_info['model']
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print(f"Evaluating fine-tuned model: {fine_tuned_model}")
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pricer = SmartPricer(fine_tuned_model=fine_tuned_model)
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tester = ConfidenceAwareTester(
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pricer,
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test_data[:100],
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title=f"Fine-tuned Smart Pricer ({fine_tuned_model})",
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size=100
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)
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results = tester.run_enhanced_test()
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if results:
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avg_error = np.mean([r['error'] for r in results])
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avg_confidence = np.mean([r['confidence'] for r in results])
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high_conf_results = [r for r in results if r['confidence'] > 0.7]
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high_conf_error = np.mean([r['error'] for r in high_conf_results]) if high_conf_results else float('inf')
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summary = {
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'model_id': fine_tuned_model,
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'total_predictions': len(results),
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'average_error': avg_error,
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'average_confidence': avg_confidence,
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'high_confidence_count': len(high_conf_results),
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'high_confidence_error': high_conf_error,
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'job_id': job_id
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}
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print(f"\nEVALUATION SUMMARY:")
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print(f"Average Error: ${avg_error:.2f}")
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print(f"Average Confidence: {avg_confidence:.2f}")
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print(f"High Confidence Predictions: {len(high_conf_results)}")
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print(f"High Confidence Error: ${high_conf_error:.2f}")
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return summary
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return {}
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def quick_fine_tune_demo(train_size: int = 200, val_size: int = 50):
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print("Smart Pricer Fine-Tuning Demo")
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print("=" * 50)
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try:
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with open('train.pkl', 'rb') as file:
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train_data = pickle.load(file)
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with open('test.pkl', 'rb') as file:
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test_data = pickle.load(file)
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print(f"Loaded training data: {len(train_data)} items")
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print(f"Loaded test data: {len(test_data)} items")
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except FileNotFoundError:
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print("Training data not found. Make sure train.pkl and test.pkl are in current directory.")
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return
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train_items = train_data[:train_size]
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val_items = train_data[train_size:train_size + val_size]
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print(f"Using {len(train_items)} training items, {len(val_items)} validation items")
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fine_tuner = SmartFineTuner()
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train_file, val_file = fine_tuner.create_training_files(
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train_items, val_items, enhanced=True
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)
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print(f"Created training files: {train_file}, {val_file}")
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print(f"\nTo start fine-tuning, uncomment the following lines:")
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print(f"job_id = fine_tuner.start_fine_tuning('{train_file}', '{val_file}')")
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print(f"# Wait for job to complete...")
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print(f"# results = fine_tuner.evaluate_fine_tuned_model(test_data, job_id)")
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print(f"\nDemo with base model (no fine-tuning):")
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pricer = SmartPricer()
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tester = ConfidenceAwareTester(pricer, test_data[:25], size=25)
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tester.run_enhanced_test()
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def main():
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import argparse
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parser = argparse.ArgumentParser(description='Smart Pricer Fine-Tuning')
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parser.add_argument('--demo', action='store_true', help='Run demo mode')
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parser.add_argument('--train-size', type=int, default=200, help='Training set size')
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parser.add_argument('--val-size', type=int, default=50, help='Validation set size')
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parser.add_argument('--evaluate', type=str, help='Evaluate existing model by job ID')
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args = parser.parse_args()
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if args.demo:
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quick_fine_tune_demo(args.train_size, args.val_size)
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elif args.evaluate:
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try:
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with open('test.pkl', 'rb') as file:
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test_data = pickle.load(file)
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fine_tuner = SmartFineTuner()
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fine_tuner.evaluate_fine_tuned_model(test_data, args.evaluate)
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except FileNotFoundError:
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print("Test data not found. Make sure test.pkl is in current directory.")
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else:
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print("Use --demo to run demo or --evaluate <job_id> to evaluate existing model")
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if __name__ == "__main__":
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main()
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