# 🚀 Centering Theory Optimizasyon Yol Haritası

## 🎯 Amaç: Kalite ↑ + Hız ↑ + Bellek ↓

### **PHASE 1: HIZLI KAZANIMLAR (5-15 dakika)**

#### A. Centering Cache Sistemi
```python
# Önceden hesaplanan center'ları cache'le
@lru_cache(maxsize=500)
def cached_extract_centers(sentence_hash: str) -> List[str]:
    # SpaCy analizi sonuçlarını cache'le
    pass

# Transition pattern'ları cache'le  
@lru_cache(maxsize=200)
def cached_transition_analysis(prev_sentence: str, curr_sentence: str):
    pass
```

#### B. Smart Text Sampling
```python
# Geliştirme için küçük text sample
MAX_LINES_DEV = 2000  # 37,580 → 2,000 line
MAX_LINES_PROD = 15000  # Production için optimize

# Quality-based sampling (high coherence scores)
def sample_high_quality_text(text_file, quality_threshold=0.7):
    pass
```

#### C. Lightweight SpaCy
```python
# Sadece gerekli component'ları yükle
nlp = spacy.load("en_core_web_sm", disable=["ner", "textcat", "lemmatizer"])
# Ya da centering için minimal pipeline
nlp = spacy.blank("en")
nlp.add_pipe("tagger")
nlp.add_pipe("parser")
```

### **PHASE 2: CENTERING THEORY OPTIMIZASYONU (15-30 dakika)**

#### A. Intelligent Center Prediction
```python
class SmartCenterPredictor:
    def __init__(self):
        # Pattern-based center prediction
        self.center_patterns = {}
        self.transition_history = []
    
    def predict_next_center(self, context: List[str]) -> Optional[str]:
        # ML-based center prediction
        # POS pattern matching
        # Frequency-based center selection
        pass
    
    def update_patterns(self, actual_center: str, predicted: str):
        # Online learning for center patterns
        pass
```

#### B. Batch Center Analysis  
```python
def analyze_centers_batch(sentences: List[str], batch_size=10):
    """Batch olarak center analizi - SpaCy pipe kullan"""
    for batch in chunked(sentences, batch_size):
        # SpaCy pipe kullanarak batch process
        docs = list(nlp.pipe(batch))
        # Centers extract et
        pass
```

#### C. Transition Quality Scoring
```python
class TransitionQualityScorer:
    def __init__(self):
        # Transition kalite skorları
        self.transition_weights = {
            'CONTINUE': 1.0,    # En iyi
            'RETAIN': 0.8,      # İyi
            'SMOOTH_SHIFT': 0.6, # Orta
            'ROUGH_SHIFT': 0.3   # Düşük
        }
    
    def score_generation_quality(self, transitions: List[str]) -> float:
        # Real-time quality scoring
        pass
    
    def suggest_improvement(self, current_transition: str) -> str:
        # Daha iyi transition öner
        pass
```

### **PHASE 3: HYBRID GENERATION ENGINE (30-60 dakika)**

#### A. Multi-Level Text Generation
```python
class HybridGenerationEngine:
    def __init__(self):
        self.quick_generator = FastNgramGenerator()  # Hızlı, düşük kalite
        self.quality_generator = CenteringGenerator() # Yavaş, yüksek kalite
        self.hybrid_generator = SmartHybridGenerator() # Balanced
    
    def generate_adaptive(self, context: str, quality_target: float, time_budget: float):
        # Quality target ve time budget'a göre engine seç
        if time_budget < 5.0:  # 5 saniyeden az
            return self.quick_generator.generate(context)
        elif quality_target > 0.8:  # Yüksek kalite
            return self.quality_generator.generate(context)
        else:
            return self.hybrid_generator.generate(context, quality_target, time_budget)
```

#### B. Progressive Quality Enhancement
```python
class ProgressiveQualityEnhancer:
    def generate_progressive(self, input_text: str, iterations=3):
        """
        1. İlk iteration: Hızlı n-gram generation  
        2. İkinci iteration: Centering theory application
        3. Üçüncü iteration: T5 grammar correction + coherence check
        """
        result = input_text
        
        # Phase 1: Fast generation
        result = self.fast_generate(result)
        
        # Phase 2: Apply centering theory  
        result = self.apply_centering(result)
        
        # Phase 3: Polish with T5 (sadece gerekirse)
        if self.needs_grammar_correction(result):
            result = self.correct_grammar_t5(result)
            
        return result
```

### **PHASE 4: MEMORY & PERFORMANCE OPTIMIZATION**

#### A. Smart Caching Strategy
```python
class SmartCache:
    def __init__(self):
        # Multi-level cache
        self.l1_cache = {}  # Frequently used (100 items)
        self.l2_cache = {}  # Recent items (500 items)  
        self.l3_cache = {}  # Pattern cache (1000 items)
    
    def get_cached_result(self, key: str, level: int = 1):
        # Cache hit strategy
        pass
    
    def evict_smart(self):
        # LRU + frequency-based eviction
        pass
```

#### B. Asynchronous Processing
```python
class AsyncCenteringProcessor:
    async def process_sentences_async(self, sentences: List[str]):
        # Background'da center analysis
        # Main thread'de generation continue et
        pass
    
    async def background_quality_check(self, generated_text: str):
        # Generate ederken background'da quality check
        pass
```

## 🎯 **Kalite Metrikleri & KPI'lar**

### Centering Theory Kalite Metrikleri:
```python
class CenteringQualityMetrics:
    def calculate_coherence_score(self, text: str) -> Dict[str, float]:
        return {
            'centering_continuity': 0.0,      # Center continuation oranı
            'transition_smoothness': 0.0,     # Smooth transition oranı  
            'entity_consistency': 0.0,        # Entity tutarlılığı
            'pronoun_resolution': 0.0,        # Pronoun resolution kalitesi
            'overall_coherence': 0.0          # Genel coherence skoru
        }
    
    def benchmark_generation(self, num_samples=100):
        # Quality vs Speed benchmark
        pass
```

### Performance KPI'lar:
- **Generation Speed**: < 2 saniye/paragraph
- **Memory Usage**: < 1.5GB steady state  
- **Cache Hit Rate**: > 70%
- **Coherence Score**: > 0.75
- **Transition Quality**: > 80% CONTINUE/RETAIN

## 📈 **Beklenen Sonuçlar**

### Phase 1 Sonrası:
- **Hız**: 95% iyileşme (501s → ~25s)
- **Bellek**: 60% azalma (3.58GB → ~1.4GB)
- **Kalite**: %5-10 artış

### Phase 2 Sonrası:
- **Coherence Score**: 0.6 → 0.8
- **Center Continuity**: +40%
- **Generation Speed**: 2x hızlanma

### Phase 3 Sonrası:
- **Adaptive Quality**: Real-time quality control
- **Hybrid Generation**: Time/Quality trade-off
- **Progressive Enhancement**: İsteğe bağlı polish

### Phase 4 Sonrası:
- **Total Memory**: < 1GB
- **Processing Time**: < 2 dakika full pipeline
- **Cache Efficiency**: 80%+ hit rate

## 🔧 **Implementation Öncelikleri**

### HIGH PRIORITY (Hemen):
1. Text sampling (MAX_LINES = 2000)
2. SpaCy lightweight config  
3. Center extraction cache

### MEDIUM PRIORITY (Bu hafta):
1. Batch center analysis
2. Transition quality scoring
3. Smart cache system

### LOW PRIORITY (Gelecek):
1. Async processing
2. ML-based center prediction
3. Advanced quality metrics

## 📊 **Monitoring Dashboard**

```python
class CenteringDashboard:
    def display_metrics(self):
        """
        - Real-time coherence score
        - Transition type distribution  
        - Memory usage timeline
        - Generation speed histogram
        - Cache hit rates
        - Quality trends
        """
        pass
```

Bu roadmap ile hem **kalite artışı** hem de **süre optimizasyonu** sağlayabiliriz! 🚀
