# Advanced Modules Documentation

AutoPipelineDoctor (autopd) includes a set of advanced, cutting-edge modules that provide sophisticated capabilities for AI model training monitoring, optimization, and analysis. These modules are designed for elite AI developers and researchers at organizations like OpenAI, Google DeepMind, Anthropic, Meta FAIR, and NVIDIA.

## Enabling Advanced Modules

To enable advanced modules, pass `enable_advanced=True` when initializing the Doctor:

```python
from autopd import Doctor

doctor = Doctor(model, optimizer, dataloader, enable_advanced=True)
doctor.watch(train_loop)
```

## Available Advanced Modules

### 1. Neuro-Behavioral Pattern Clustering (NBPC)

Analyzes time-series training telemetry to identify high-risk behavioral clusters and detect problematic training patterns.

```python
# Access NBPC functionality
clusters = doctor.advanced_modules['nbpc'].get_behavioral_clusters()
warnings = doctor.advanced_modules['nbpc'].get_active_warnings()

# Visualize clusters
doctor.advanced_modules['nbpc'].visualize_clusters(output_file='clusters.html')
```

### 2. Autonomous Optimization Loop Injection (AOLI)

Monitors training dynamics and automatically injects performance-enhancing changes in real-time.

```python
# Configure AOLI
doctor.advanced_modules['aoli'].set_risk_level('medium')  # 'low', 'medium', 'high'
doctor.advanced_modules['aoli'].enable_auto_apply()

# Manually apply optimizations
doctor.advanced_modules['aoli'].apply_optimization('amp')
doctor.advanced_modules['aoli'].apply_optimization('gradient_checkpointing')
```

### 3. Causal Fault Tree Analysis (CFTA)

Traces backward through pipeline execution to identify root causes of failures and performance issues.

```python
# Generate fault tree for a specific issue
fault_tree = doctor.advanced_modules['cfta'].analyze_fault('oom_error')
doctor.advanced_modules['cfta'].visualize_fault_tree(fault_tree, output_file='fault_tree.html')

# Get root causes
root_causes = doctor.advanced_modules['cfta'].get_root_causes(fault_tree)
```

### 4. Latent Loss Surface Mapping

Creates 3D visualizations of the loss landscape to identify problematic training regions.

```python
# Generate loss surface map
doctor.advanced_modules['llsm'].generate_loss_map()
doctor.advanced_modules['llsm'].visualize_loss_surface(output_file='loss_surface.html')

# Analyze loss surface features
features = doctor.advanced_modules['llsm'].analyze_surface_features()
```

### 5. Synthetic Model Shadowing

Runs lightweight proxy models alongside the main model to predict training outcomes and detect issues early.

```python
# Configure shadow model
doctor.advanced_modules['sms'].set_shadow_type('distilled')  # 'distilled', 'quantized', 'pruned'
doctor.advanced_modules['sms'].set_update_frequency(50)

# Get predictions from shadow model
predictions = doctor.advanced_modules['sms'].get_shadow_predictions()
comparison = doctor.advanced_modules['sms'].compare_with_main_model()
```

### 6. Hardware-Aware Learning Curve Forecasting

Predicts training time, resource allocation, and performance based on hardware characteristics and model architecture.

```python
# Get forecasts
epoch_time_forecast = doctor.advanced_modules['halcf'].forecast_epoch_time()
resource_forecast = doctor.advanced_modules['halcf'].forecast_resource_needs()
saturation_forecast = doctor.advanced_modules['halcf'].forecast_saturation_point()

# Visualize forecasts
doctor.advanced_modules['halcf'].visualize_forecasts(output_file='forecasts.html')
```

### 7. Anomaly-Activated Alert System (AAA-Sentry)

Monitors for sudden spikes in resource usage, loss, latency, or idle periods and sends alerts through various channels.

```python
# Configure alert system
doctor.advanced_modules['aaas'].set_sensitivity('medium')  # 'low', 'medium', 'high'
doctor.advanced_modules['aaas'].add_alert_channel('slack', webhook_url='https://hooks.slack.com/...')
doctor.advanced_modules['aaas'].add_alert_channel('email', config={'to': 'user@example.com'})

# Manually trigger snapshot
doctor.advanced_modules['aaas'].create_snapshot('manual_snapshot')
```

### 8. LLM-Connected Think Tank Mode

Creates a multi-agent reasoning system to analyze pipeline state, debate optimization strategies, and cite relevant research.

```python
# Configure think tank
doctor.advanced_modules['llmtt'].set_provider('openai')  # 'openai', 'anthropic', 'local'
doctor.advanced_modules['llmtt'].set_num_agents(3)

# Run analysis
analysis = doctor.analyze_with_think_tank("Why is my model experiencing gradient vanishing?")
recommendations = doctor.advanced_modules['llmtt'].get_ranked_recommendations()
```

### 9. DNA Tracker for Experiment Lineage

Assigns a unique genetic fingerprint to every run and tracks the full ancestry of models.

```python
# Get DNA information
dna_report = doctor.get_model_dna()
lineage = doctor.advanced_modules['dna'].get_model_lineage()

# Visualize evolution
doctor.advanced_modules['dna'].visualize_evolution_tree(output_file='evolution.html')
```

### 10. Real-Time Model Cognition Visualization (MindScope)

Visualizes what the model "pays attention to" during training, including attention heatmaps and neuron firing graphs.

```python
# Configure visualization
doctor.advanced_modules['rtmcv'].set_visualization_type('all')  # 'attention', 'neurons', 'activations', 'all'
doctor.advanced_modules['rtmcv'].set_update_frequency(10)

# Generate visualizations
attention_map = doctor.visualize_model_cognition(layer_name='transformer.layer.5')
doctor.advanced_modules['rtmcv'].visualize_attention(output_file='attention.html')
```

### 11. Quantum-Inspired Optimization Pathfinder

Uses quantum-inspired optimization algorithms to efficiently explore hyperparameter spaces and find optimal configurations.

```python
# Configure optimization
doctor.advanced_modules['qiop'].set_num_particles(20)
doctor.advanced_modules['qiop'].set_search_space('hyperparameters')  # 'hyperparameters', 'architecture', 'both'

# Run optimization
results = doctor.optimize_with_quantum_pathfinder(target='performance', iterations=10)
doctor.advanced_modules['qiop'].visualize_optimization_path(output_file='optimization_path.html')
```

### 12. Federated Training Synchronization Monitor

Monitors and optimizes synchronization in federated learning environments, detecting model drift and optimizing communication patterns.

```python
# Configure monitor
doctor.advanced_modules['ftsm'].set_num_nodes(4)
doctor.advanced_modules['ftsm'].set_sync_strategy('adaptive')  # 'fixed', 'adaptive', 'dynamic'

# Get monitoring information
report = doctor.monitor_federated_training()
drift_analysis = doctor.advanced_modules['ftsm'].analyze_model_drift()
doctor.advanced_modules['ftsm'].visualize_node_synchronization(output_file='sync.html')
```

### 13. Adversarial Robustness Analyzer

Provides comprehensive adversarial attack testing, vulnerability detection, and robustness enhancement for deep learning models.

```python
# Configure analyzer
doctor.advanced_modules['ara'].set_attack_methods(['fgsm', 'pgd', 'deepfool'])
doctor.advanced_modules['ara'].set_defense_methods(['adversarial_training', 'input_transformation'])
doctor.advanced_modules['ara'].set_auto_enhance(True)

# Run analysis
robustness_report = doctor.analyze_adversarial_robustness()
vulnerabilities = doctor.advanced_modules['ara'].get_vulnerabilities()
doctor.advanced_modules['ara'].visualize_robustness(output_file='robustness.html')
```

## Configuration Options

You can configure advanced modules through the `config` parameter when initializing the Doctor:

```python
config = {
    # NBPC configuration
    'nbpc_history_length': 100,
    'nbpc_cluster_threshold': 0.7,
    'nbpc_warning_threshold': 0.8,
    
    # AOLI configuration
    'aoli_auto_apply': False,
    'aoli_risk_level': 'medium',
    
    # CFTA configuration
    'cfta_max_depth': 5,
    'cfta_min_confidence': 0.7,
    
    # LLSM configuration
    'llsm_resolution': 20,
    'llsm_update_frequency': 100,
    
    # SMS configuration
    'sms_shadow_type': 'distilled',
    'sms_update_frequency': 50,
    
    # HALCF configuration
    'dataset_size': 50000,
    'batch_size': 32,
    
    # AAAS configuration
    'aaas_alert_channels': ['cli'],
    'aaas_sensitivity': 'medium',
    'aaas_webhook_url': None,
    'aaas_email_config': None,
    
    # LLMTT configuration
    'llmtt_api_key': 'your-api-key',
    'llmtt_provider': 'openai',
    'llmtt_num_agents': 3,
    'llmtt_max_tokens': 1000,
    
    # DNA Tracker configuration
    'dna_track_checkpoints': True,
    'dna_track_code': True,
    'dna_track_data': False,
    
    # RTMCV configuration
    'rtmcv_update_frequency': 10,
    'rtmcv_visualization_type': 'all',
    
    # QIOP configuration
    'qiop_num_particles': 20,
    'qiop_search_space': 'hyperparameters',
    'qiop_optimization_target': 'performance',
    
    # FTSM configuration
    'ftsm_num_nodes': 1,
    'ftsm_sync_strategy': 'adaptive',
    'ftsm_drift_threshold': 0.1,
    
    # ARA configuration
    'ara_attack_methods': ['fgsm', 'pgd'],
    'ara_defense_methods': [],
    'ara_robustness_metrics': ['empirical_robustness'],
    'ara_auto_enhance': False,
}

doctor = Doctor(model, optimizer, dataloader, enable_advanced=True, config=config)
```

## Convenience Methods

The Doctor class provides convenience methods for accessing advanced module functionality:

```python
# LLM Think Tank
analysis = doctor.analyze_with_think_tank("Why is my model experiencing gradient vanishing?")

# Model Cognition Visualization
attention_map = doctor.visualize_model_cognition(layer_name='transformer.layer.5')

# DNA Tracking
dna_report = doctor.get_model_dna()

# Adversarial Robustness
robustness_report = doctor.analyze_adversarial_robustness()

# Quantum Optimization
results = doctor.optimize_with_quantum_pathfinder(target='performance', iterations=10)

# Federated Training
report = doctor.monitor_federated_training()
```

## Advanced Recommendations

When advanced modules are enabled, the `get_recommendations()` method will include recommendations from all advanced modules:

```python
# Get recommendations from all modules
recommendations = doctor.get_recommendations()

# Filter recommendations by source
aoli_recommendations = [r for r in recommendations if r.get('source') == 'aoli']
```

## Advanced Visualizations

The `visualize()` method will include visualizations from advanced modules when `visualization_type='all'` or `visualization_type='advanced'`:

```python
# Get all visualizations including advanced ones
visualization = doctor.visualize(output_file='visualization.html', visualization_type='all')

# Get only advanced visualizations
advanced_visualization = doctor.visualize(visualization_type='advanced')
```

## Advanced Reports

The `generate_report()` method will include reports from advanced modules when `report_type='all'` or `report_type='advanced'`:

```python
# Generate comprehensive report including advanced modules
report = doctor.generate_report(output_file='report.html', report_type='all')

# Generate only advanced report
advanced_report = doctor.generate_report(report_type='advanced')
```
