File size: 3,187 Bytes
f3f45a4 3fef4cf f3f45a4 3fef4cf f3f45a4 3fef4cf f3f45a4 3fef4cf f3f45a4 3fef4cf f3f45a4 3fef4cf f3f45a4 3fef4cf f3f45a4 3fef4cf f3f45a4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
# Training Report - Ensemble
Generated: 2025-09-08 18:12:05
## Overview
- **Command**: `ensemble`
- **Training Duration**: 6144.87 seconds (102.4 minutes)
- **Output Directory**: `output/ensemble_20250908_162940`
## Dataset Information
- **Total Records**: 25,512
- **Training Steps per Epoch**: 637
- **Validation Steps per Epoch**: 159
### Vocabulary Sizes
- **Stations**: 6 unique stations
- **Routes**: 13 unique routes
- **Tracks**: 13 unique tracks (prediction targets)
## Training Configuration
- **Num Models**: 6
- **Epochs**: 1000
- **Batch Size**: 32
- **Base Learning Rate**: 0.001
- **Dataset Size**: 25512
- **Bagging Fraction**: 1.0
- **Seed Base**: 42
## Final Performance Metrics
- **Average Validation Loss**: 0.9233
- **Average Validation Accuracy**: 0.7460
- **Best Individual Accuracy**: 0.7720
- **Worst Individual Accuracy**: 0.7256
- **Ensemble Std Accuracy**: 0.0181
## Additional Information
- **Individual Model Metrics**: {'model_index': 0, 'validation_loss': 0.8818949460983276, 'validation_accuracy': 0.7720125913619995, 'learning_rate': 0.0011677725896206085, 'parameters': 53384}, {'model_index': 1, 'validation_loss': 0.9238271713256836, 'validation_accuracy': 0.7682783007621765, 'learning_rate': 0.0010442193817105285, 'parameters': 156552}, {'model_index': 2, 'validation_loss': 0.9241461753845215, 'validation_accuracy': 0.7399764060974121, 'learning_rate': 0.00096484374873539, 'parameters': 14856}, {'model_index': 3, 'validation_loss': 0.9481979608535767, 'validation_accuracy': 0.7256289124488831, 'learning_rate': 0.0009162122256532111, 'parameters': 14856}, {'model_index': 4, 'validation_loss': 0.9160543084144592, 'validation_accuracy': 0.7421383857727051, 'learning_rate': 0.0008199605784692232, 'parameters': 14856}, {'model_index': 5, 'validation_loss': 0.9454122185707092, 'validation_accuracy': 0.7279874086380005, 'learning_rate': 0.0009672535936195217, 'parameters': 14856}
- **Ensemble Strategy**: Diverse architectures (deep, wide, standard)
- **Learning Rate Variation**: 0.8x to 1.2x base rate with random variation
- **Total Parameters**: 269360
### Temperature Scaling
- **Temperature**: 1.5000
- **Uncalibrated Nll**: 1.9108
- **Calibrated Nll**: 1.8276
- **Uncalibrated Ece**: 0.0939
- **Calibrated Ece**: 0.0302
## Dataset Schema
The model was trained on MBTA track assignment data with the following features:
- **Categorical Features**: station_id, route_id, direction_id
- **Temporal Features**: hour, minute, day_of_week (cyclically encoded)
- **Target**: track_number (classification with 13 classes)
## Model Architecture
- Embedding layers for categorical features
- Cyclical time encoding (sin/cos) for temporal patterns
- Dense layers with dropout regularization
- Softmax output for multi-class track prediction
## Usage
To load and use this model:
```python
import keras
# Load for inference (optimizer not saved):
model = keras.models.load_model('track_prediction_ensemble_final.keras', compile=False)
```
---
*Report generated by imt-ml training pipeline*
|