Update README.md

README.md

@@ -1,179 +1,179 @@
----
-license: mit
-language:
-- en
-tags:
-- neural-network
-- route-optimization
-- pytorch
-- landmarks
-- cmu
-- campus-exploration
-size_categories:
-- n<1K
----
-
-# ML-Enhanced Route Optimization for CMU Landmarks
-
-## Model Description
-
-This is a **fine-tuned** machine learning approach to route optimization that combines traditional routing algorithms with ML-based preference learning. It uses a neural network to optimize routes considering user preferences and geographic constraints.
-
-## Model Details
-
-### Model Type
-- **Architecture**: Neural Network + Traditional Routing Hybrid
-- **Training**: Fine-tuned approach with preference learning
-- **Input**: Landmark features, user preferences, distance constraints
-- **Output**: Optimized route with preference satisfaction
-
-### Model Components
-
-#### 1. Neural Network Component
-```python
-class RouteOptimizer(nn.Module):
-    def __init__(self, input_dim):
-        super().__init__()
-        self.fc1 = nn.Linear(input_dim, 64)
-        self.fc2 = nn.Linear(64, 32)
-        self.fc3 = nn.Linear(32, 1)
-        self.relu = nn.ReLU()
-        self.dropout = nn.Dropout(0.2)
-```
-
-#### 2. Feature Extraction
-- **Distance**: Geographic distance to current position
-- **Time Cost**: Dwell time at landmark
-- **Class Alignment**: Preference alignment score
-- **Rating Factor**: Normalized landmark rating
-
-#### 3. Hybrid Optimization
-```
-1. Extract ML features for each landmark
-2. Score landmarks using neural network
-3. Apply nearest neighbor ordering with ML scores
-4. Post-process with 2-opt improvement
-```
-
-## Intended Use
-
-### Primary Use Cases
-- Route optimization with user preference consideration
-- Personalized campus exploration planning
-- ML-enhanced itinerary optimization
-- Preference-aware landmark routing
-
-### Out-of-Scope Use Cases
-- Real-time route adaptation
-- Multi-user collaborative planning
-- Cross-campus routing
-
-## Performance Metrics
-
-### Model Performance
-```
-Training Accuracy: 85-90% preference satisfaction
-Route Efficiency: 5-15% improvement over traditional
-Preference Satisfaction: 70-85% user preference alignment
-Execution Time: < 50ms for 20 landmarks
-```
-
-### Comparison Metrics
-- **Distance Efficiency**: 5-15% better than traditional routing
-- **Preference Satisfaction**: 70-85% alignment with user preferences
-- **Route Quality**: Balanced optimization of distance and preferences
-
-## Training Details
-
-### Training Data
-- **Source**: CMU landmarks with user preference simulations
-- **Features**: Geographic, temporal, and preference-based features
-- **Validation**: Cross-validation on landmark subsets
-
-### Training Procedure
-- **Architecture**: 3-layer neural network with dropout
-- **Optimization**: Gradient descent with preference weighting
-- **Regularization**: Dropout (0.2) to prevent overfitting
-
-## Limitations and Bias
-
-- **Training Data**: Limited to CMU campus landmarks
-- **Preference Learning**: May inherit biases from training preferences
-- **Static Model**: Doesn't adapt to real-time user feedback
-- **Computational Cost**: Higher than traditional methods
-
-## Ethical Considerations
-
-- **Bias**: May reflect biases in training preference data
-- **Transparency**: ML decisions are less interpretable than traditional methods
-- **Fairness**: Equal consideration for all landmark types
-
-## How to Use
-
-```python
-from model import MLRouteOptimizer, load_model_from_data
-
-# Load model from landmarks data
-optimizer = load_model_from_data('data/landmarks.json')
-
-# Optimize route with ML
-selected_indices = [0, 5, 10, 15, 20]  # Landmark indices to visit
-start_idx = 0
-time_budget = 120.0
-preferences = {
-    'selected_classes': ['Culture', 'Research'],
-    'indoor_pref': 'indoor',
-    'min_rating': 4.0
-}
-
-# Get optimized route
-optimized_route = optimizer.optimize_route(
-    selected_indices, start_idx, time_budget, preferences
-)
-
-print(f"Optimized route: {optimized_route}")
-
-# Compare with traditional method
-comparison = optimizer.compare_routing_methods(
-    selected_indices, start_idx, preferences, time_budget
-)
-
-print(f"Distance improvement: {comparison['comparison']['distance_improvement']:.1%}")
-print(f"Preference improvement: {comparison['comparison']['preference_improvement']:.3f}")
-```
-
-## Model Files
-
-- `model.py`: Main model implementation
-- `README.md`: This model card
-
-## Feature Importance
-
-Based on model analysis:
-1. **Class Alignment** (40%): User preference satisfaction
-2. **Rating Factor** (30%): Landmark quality score
-3. **Distance** (20%): Geographic efficiency
-4. **Time Cost** (10%): Temporal constraints
-
-## Integration with Traditional Methods
-
-The ML router:
-1. **Enhances** traditional nearest neighbor with ML scoring
-2. **Improves** traditional 2-opt with preference-aware optimization
-3. **Falls back** to traditional methods when ML is unavailable
-4. **Compares** performance against traditional baselines
-
-## Citation
-
-```bibtex
-@misc{cmu-explorer-ml-router,
-  title={ML-Enhanced Route Optimization},
-  author={CMU Explorer Team},
-  year={2024},
-  url={https://huggingface.co/spaces/ysakhale/Tartan-Explore}
-}
-```
-
-## Model Card Contact
-
-For questions about this model, please refer to the [CMU Explorer Space](https://huggingface.co/spaces/ysakhale/Tartan-Explore).
+---
+license: mit
+language:
+- en
+tags:
+- neural-network
+- route-optimization
+- pytorch
+- landmarks
+- cmu
+- campus-exploration
+size_categories:
+- n<1K
+---
+
+# ML-Enhanced Route Optimization for CMU Landmarks
+
+## Model Description
+
+This is a **fine-tuned** machine learning approach to route optimization that combines traditional routing algorithms with ML-based preference learning. It uses a neural network to optimize routes considering user preferences and geographic constraints.
+
+## Model Details
+
+### Model Type
+- **Architecture**: Neural Network + Traditional Routing Hybrid
+- **Training**: Fine-tuned approach with preference learning
+- **Input**: Landmark features, user preferences, distance constraints
+- **Output**: Optimized route with preference satisfaction
+
+### Model Components
+
+#### 1. Neural Network Component
+```python
+class RouteOptimizer(nn.Module):
+    def __init__(self, input_dim):
+        super().__init__()
+        self.fc1 = nn.Linear(input_dim, 64)
+        self.fc2 = nn.Linear(64, 32)
+        self.fc3 = nn.Linear(32, 1)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(0.2)
+```
+
+#### 2. Feature Extraction
+- **Distance**: Geographic distance to current position
+- **Time Cost**: Dwell time at landmark
+- **Class Alignment**: Preference alignment score
+- **Rating Factor**: Normalized landmark rating
+
+#### 3. Hybrid Optimization
+```
+1. Extract ML features for each landmark
+2. Score landmarks using neural network
+3. Apply nearest neighbor ordering with ML scores
+4. Post-process with 2-opt improvement
+```
+
+## Intended Use
+
+### Primary Use Cases
+- Route optimization with user preference consideration
+- Personalized campus exploration planning
+- ML-enhanced itinerary optimization
+- Preference-aware landmark routing
+
+### Out-of-Scope Use Cases
+- Real-time route adaptation
+- Multi-user collaborative planning
+- Cross-campus routing
+
+## Performance Metrics
+
+### Model Performance
+```
+Training Accuracy: 85-90% preference satisfaction
+Route Efficiency: 5-15% improvement over traditional
+Preference Satisfaction: 70-85% user preference alignment
+Execution Time: < 50ms for 20 landmarks
+```
+
+### Comparison Metrics
+- **Distance Efficiency**: 5-15% better than traditional routing
+- **Preference Satisfaction**: 70-85% alignment with user preferences
+- **Route Quality**: Balanced optimization of distance and preferences
+
+## Training Details
+
+### Training Data
+- **Source**: CMU landmarks with user preference simulations
+- **Features**: Geographic, temporal, and preference-based features
+- **Validation**: Cross-validation on landmark subsets
+
+### Training Procedure
+- **Architecture**: 3-layer neural network with dropout
+- **Optimization**: Gradient descent with preference weighting
+- **Regularization**: Dropout (0.2) to prevent overfitting
+
+## Limitations and Bias
+
+- **Training Data**: Limited to CMU campus landmarks
+- **Preference Learning**: May inherit biases from training preferences
+- **Static Model**: Doesn't adapt to real-time user feedback
+- **Computational Cost**: Higher than traditional methods
+
+## Ethical Considerations
+
+- **Bias**: May reflect biases in training preference data
+- **Transparency**: ML decisions are less interpretable than traditional methods
+- **Fairness**: Equal consideration for all landmark types
+
+## How to Use
+
+```python
+from model import MLRouteOptimizer, load_model_from_data
+
+# Load model from landmarks data
+optimizer = load_model_from_data('data/landmarks.json')
+
+# Optimize route with ML
+selected_indices = [0, 5, 10, 15, 20]  # Landmark indices to visit
+start_idx = 0
+time_budget = 120.0
+preferences = {
+    'selected_classes': ['Culture', 'Research'],
+    'indoor_pref': 'indoor',
+    'min_rating': 4.0
+}
+
+# Get optimized route
+optimized_route = optimizer.optimize_route(
+    selected_indices, start_idx, time_budget, preferences
+)
+
+print(f"Optimized route: {optimized_route}")
+
+# Compare with traditional method
+comparison = optimizer.compare_routing_methods(
+    selected_indices, start_idx, preferences, time_budget
+)
+
+print(f"Distance improvement: {comparison['comparison']['distance_improvement']:.1%}")
+print(f"Preference improvement: {comparison['comparison']['preference_improvement']:.3f}")
+```
+
+## Model Files
+
+- `model.py`: Main model implementation
+- `README.md`: This model card
+
+## Feature Importance
+
+Based on model analysis:
+1. **Class Alignment** (40%): User preference satisfaction
+2. **Rating Factor** (30%): Landmark quality score
+3. **Distance** (20%): Geographic efficiency
+4. **Time Cost** (10%): Temporal constraints
+
+## Integration with Traditional Methods
+
+The ML router:
+1. **Enhances** traditional nearest neighbor with ML scoring
+2. **Improves** traditional 2-opt with preference-aware optimization
+3. **Falls back** to traditional methods when ML is unavailable
+4. **Compares** performance against traditional baselines
+
+## Citation
+
+```bibtex
+@misc{cmu-explorer-ml-router,
+  title={ML-Enhanced Route Optimization},
+  author={Yash Sakhale, Faiyaz Azam},
+  year={2025},
+  url={https://huggingface.co/spaces/ysakhale/Tartan-Explore}
+}
+```
+
+## Model Card Contact
+
+For questions about this model, please refer to the [CMU Explorer Space](https://huggingface.co/spaces/ysakhale/Tartan-Explore).