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from datetime import datetime
from pathlib import Path
from typing import List, Dict, Optional
import json
import re
import hashlib
import pickle
import spacy
from tqdm import tqdm
from pipeline_config import PipelineConfig
from dialogue_augmenter import DialogueAugmenter
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
class ProcessingPipeline:
"""
Complete pipeline combining validation, optimization, and augmentation.
"""
def __init__(self, config: Optional[PipelineConfig] = None):
self.config = config or PipelineConfig()
self.nlp = spacy.load("en_core_web_sm", disable=['parser', 'ner'])
self.augmenter = DialogueAugmenter(self.nlp, self.config)
self.num_threads = self.config.batch_size
self.cache_dir = Path("./cache")
self.cache_dir.mkdir(exist_ok=True)
def process_dataset(self, dialogues: List[Dict]) -> List[Dict]:
"""
Process entire dataset through the pipeline.
"""
print(f"Processing {len(dialogues)} dialogues")
start_time = datetime.now()
# Check cache
if self.config.use_cache:
cache_path = self._get_cache_path(dialogues)
if cache_path.exists():
print("Loading from cache...")
with open(cache_path, 'rb') as f:
return pickle.load(f)
# Validate and clean
valid_dialogues = self._process_validation(
dialogues,
self._validate_and_clean_dialogue,
"validating and cleaning"
)
if not valid_dialogues:
raise ValueError("Dialogue validation resulted in an empty dataset.")
deduplicated_dialogues = self._deduplicate_dialogues(valid_dialogues)
# Augment dialogues
all_processed_dialogues = []
for dialogue in deduplicated_dialogues:
augmented = self.augmenter.augment_dialogue(dialogue)
all_processed_dialogues.extend(augmented)
# Save to cache
if self.config.use_cache:
with open(cache_path, 'wb') as f:
pickle.dump(all_processed_dialogues, f)
processing_time = datetime.now() - start_time
print(f"Processing completed in {processing_time}")
print(f"Generated {len(all_processed_dialogues)} total dialogues")
return all_processed_dialogues
def _deduplicate_dialogues(self, dialogues: List[Dict], threshold: float = 0.9) -> List[Dict]:
"""
Deduplicate dialogues based on text similarity.
"""
print("Deduplicating dialogues...")
if not dialogues:
print("No dialogues provided for deduplication.")
return []
# Combine turns into single text for similarity comparison
texts = [" ".join(turn['text'] for turn in dialogue['turns']) for dialogue in dialogues]
tfidf = TfidfVectorizer().fit_transform(texts)
sim_matrix = cosine_similarity(tfidf)
unique_indices = set()
for i, row in enumerate(sim_matrix):
if i not in unique_indices:
similar_indices = [j for j, sim in enumerate(row) if sim > threshold and j != i]
unique_indices.add(i)
unique_indices.difference_update(similar_indices)
deduplicated_dialogues = [dialogues[i] for i in unique_indices]
print(f"Deduplication complete. Reduced from {len(dialogues)} to {len(deduplicated_dialogues)} dialogues.")
return deduplicated_dialogues
def _validate_and_clean_dialogue(self, dialogue: Dict) -> Optional[Dict]:
"""
Validate and clean a single dialogue.
"""
try:
# Check required fields
if not all(field in dialogue for field in self.config.required_fields):
return None
# Process turns
cleaned_turns = []
for turn in dialogue['turns']:
if self._validate_turn(turn):
cleaned_turn = {
'speaker': turn['speaker'],
'text': self._clean_text(turn['text'])
}
cleaned_turns.append(cleaned_turn)
if cleaned_turns:
return {
'dialogue_id': dialogue['dialogue_id'],
'turns': cleaned_turns
}
return None
except Exception as e:
print(f"Error processing dialogue {dialogue.get('dialogue_id', 'unknown')}: {str(e)}")
return None
def _validate_turn(self, turn: Dict) -> bool:
"""
Validate a single speaking turn.
"""
return (
turn['speaker'] in self.config.allowed_speakers and
self.config.min_length <= len(turn['text']) <= self.config.max_length
)
def _clean_text(self, text: str) -> str:
"""
Clean and normalize text.
"""
# Remove excessive whitespace
text = re.sub(r'\s+', ' ', text.strip())
# Normalize quotes and apostrophes
text = re.sub(r'[’´`]', "'", text)
text = re.sub(r'[“”]', '"', text)
# Remove control characters
text = "".join(char for char in text if ord(char) >= 32 or char == '\n')
return text
def _process_validation(self, items: List, func, description: str) -> List:
"""
Process items sequentially with a progress bar.
"""
results = []
print(f"Starting {description}")
for item in tqdm(items, desc=description):
try:
result = func(item)
if result is not None:
results.append(result)
except Exception as e:
print(f"Error processing item: {str(e)}")
print(f"Completed {description}. Processed {len(results)} items successfully")
return results
def _get_cache_path(self, data: List[Dict]) -> Path:
"""
Generate cache file path based on data hash.
"""
data_str = json.dumps(data, sort_keys=True)
hash_value = hashlib.md5(data_str.encode()).hexdigest()
return self.cache_dir / f"cache_{hash_value}.pkl" |