new_iteration/taskmaster_processor.py

import os
import re
import json
from pathlib import Path
from typing import List, Dict, Optional, Any
from dataclasses import dataclass, field

@dataclass
class TaskmasterDialogue:
    conversation_id: str
    instruction_id: Optional[str]
    scenario: Optional[str]
    domain: Optional[str]
    turns: List[Dict[str, Any]]
    original_metadata: Dict[str, Any] = field(default_factory=dict)
    
    def __str__(self):
        return f"TaskmasterDialogue(conversation_id={self.conversation_id}, turns={len(self.turns)} turns)"
    
    def validate(self) -> bool:
        return bool(self.conversation_id and isinstance(self.turns, list))
    
class RawDataProcessingConfig:
    """
    Simple config for raw dataset processing
    """
    def __init__(
        self, 
        debug: bool = True,
        max_length: int = 512,
        min_turns: int = 2, 
        min_user_words: int = 3
    ):
        self.debug = debug
        self.max_length = max_length
        self.min_turns = min_turns
        self.min_user_words = min_user_words

class TaskmasterProcessor:
    """
    Load Taskmaster-1 dialogues, extracts domain.
    Clean, filter, save to pipeline format.
    """
    def __init__(self, config: RawDataProcessingConfig):
        self.config = config
    
    def load_taskmaster_dataset(
        self, 
        base_dir: str, 
        max_examples: Optional[int] = None
    ) -> List[TaskmasterDialogue]:
        """
        Load & parse Taskmaster-1 JSON for self-dialogs & woz-dialogs.
        """
        required_files = {
            "self-dialogs": "self-dialogs.json",
            "woz-dialogs": "woz-dialogs.json",
            "ontology": "ontology.json",
        }
        
        # Check for missing files
        missing = [k for k, v in required_files.items() if not Path(base_dir, v).exists()]
        if missing:
            raise FileNotFoundError(f"Missing Taskmaster files: {missing}")
        
        # Load ontology
        ontology_path = Path(base_dir, required_files["ontology"])
        with open(ontology_path, 'r', encoding='utf-8') as f:
            ontology = json.load(f)
            if self.config.debug:
                print(f"[TaskmasterProcessor] Loaded ontology with {len(ontology.keys())} top-level keys (unused).")
        
        dialogues: List[TaskmasterDialogue] = []
        
        # Process each file
        file_keys = ["self-dialogs", "woz-dialogs"]
        for file_key in file_keys:
            file_path = Path(base_dir, required_files[file_key])
            with open(file_path, 'r', encoding='utf-8') as f:
                raw_data = json.load(f)
            
            for d in raw_data:
                conversation_id = d.get("conversation_id", "")
                instruction_id = d.get("instruction_id", None)
                scenario_text = d.get("scenario", "")
                
                # Handle utterances
                utterances = d.get("utterances", [])
                turns = self._process_utterances(utterances)

                # Detect Domain
                domain = self._extract_domain(scenario_text, turns)

                # Build the object
                new_dlg = TaskmasterDialogue(
                    conversation_id=conversation_id,
                    instruction_id=instruction_id,
                    scenario=scenario_text,
                    domain=domain,
                    turns=turns,
                    original_metadata={}
                )
                dialogues.append(new_dlg)
                
                if max_examples and len(dialogues) >= max_examples:
                    break
        
        if self.config.debug:
            print(f"[TaskmasterProcessor] Loaded {len(dialogues)} total dialogues from Taskmaster-1.")
        return dialogues
    
    def _extract_domain(self, scenario: str, turns: List[Dict[str, str]]) -> str:
        """
        Combine scenario text + all turn texts to detect domain more robustly.
        """
        combined_text = scenario.lower()
        for turn in turns:
            txt = turn.get('text', '').lower()
            combined_text += " " + txt

        # Domain patterns
        domain_patterns = {
            'restaurant': r'\b(restaurant|dining|food|reservation|table|menu|cuisine|eat|hungry)\b',
            'movie': r'\b(movie|cinema|film|ticket|showtime|theater|flick|screening)\b',
            'ride_share': r'\b(ride|taxi|uber|lyft|car\s?service|pickup|dropoff|driver)\b',
            'coffee': r'\b(coffee|café|cafe|starbucks|espresso|latte|mocha|americano)\b',
            'pizza': r'\b(pizza|delivery|order\s?food|pepperoni|topping|pizzeria|slice)\b',
            'auto': r'\b(car|vehicle|repair|maintenance|mechanic|oil\s?change)\b'
        }

        for domain, pattern in domain_patterns.items():
            if re.search(pattern, combined_text):
                # Optional: print if debug
                if self.config.debug:
                    print(f"Matched domain: {domain} in scenario/turns")
                return domain
        
        if self.config.debug:
            print("No domain match, returning 'other'")
        return 'other'
    
    def _process_utterances(self, utterances: List[Dict[str, Any]]) -> List[Dict[str, str]]:
        """
        Convert "utterances" to a cleaned List -> (speaker, text).
        Skip lines that are numeric, too short, or empty.
        """
        cleaned_turns = []
        for utt in utterances:
            speaker = 'assistant' if utt.get('speaker') == 'ASSISTANT' else 'user'
            raw_text = utt.get('text', '').strip()
            
            # Text cleaning
            text = self._clean_text(raw_text)

            # Skip blank or numeric lines (e.g. "4 3 13")
            if not text or self._is_numeric_line(text):
                continue

            # Skip too short (no training benefit from 1-word user turns). E.g. "ok","yes", etc.
            if len(text.split()) < 3:
                continue

            # Add to cleaned turns
            cleaned_turns.append({
                'speaker': speaker,
                'text': text
            })
        return cleaned_turns
    
    def _clean_text(self, text: str) -> str:
        """
        Simple text normalization
        """
        # Strip multiple spaces, remove unnecessary punctuation
        text = re.sub(r'\s+', ' ', text)
        text = re.sub(r'([!?.,])\1+', r'\1', text)
        return text.strip()

    def _is_numeric_line(self, text: str) -> bool:
        """
        Return True if line is purely digits/punctuation/spaces, 
        e.g. "4 3 13" and similar found in Taskmaster-1 dataset.
        """
        pattern = r'^[\s]*[\d]+([\s\d.,]+)*[\s]*$'
        return bool(re.match(pattern, text))

    def filter_and_convert(self, dialogues: List[TaskmasterDialogue]) -> List[Dict]:
        """
        Filter out dialogues that don't meet min length requirements. Convert to pipeline format.
            {
              "dialogue_id": "...",
              "domain": "...",
              "turns": [ {"speaker": "user", "text": "..."}, ... ]
            }
        """
        results = []
        for dlg in dialogues:
            if not dlg.validate():
                continue
            
            # Skip if too few turns
            if len(dlg.turns) < self.config.min_turns:
                continue
            
            # Skip if any user turn is too short
            keep = True
            for turn in dlg.turns:
                if turn['speaker'] == 'user':
                    words_count = len(turn['text'].split())
                    if words_count < self.config.min_user_words:
                        keep = False
                        break
            
            if not keep:
                continue
            
            pipeline_dlg = {
                'dialogue_id': dlg.conversation_id,
                'domain': dlg.domain,
                'turns': dlg.turns  # already cleaned
            }
            results.append(pipeline_dlg)
        
        if self.config.debug:
            print(f"[TaskmasterProcessor] Filtered down to {len(results)} dialogues after cleaning.")
        return results