Spaces:
Runtime error
Runtime error
File size: 9,183 Bytes
dc610d2 | 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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | ================================================================================
EXPERT-LEVEL NASA SOH PRODUCTION PIPELINE
Stanford/MIT/Oxbridge/Imperial Best Practices
================================================================================
================================================================================
STEP 1: DATA PREPROCESSING
================================================================================
============================================================
UNIFIED DATA PREPROCESSING PIPELINE
============================================================
============================================================
PROCESSING NASA DATASETS
============================================================
Processing B0005 from NASA...
Loaded: 168 rows, 5 columns
Initial capacity: 1.8565
✓ Processed: 168 cycles, Cycle life: 355
Processing B0006 from NASA...
Loaded: 168 rows, 5 columns
Initial capacity: 2.0353
Removed 1 outlier cycles
✓ Processed: 167 cycles, Cycle life: 201
Processing B0007 from NASA...
Loaded: 168 rows, 5 columns
Initial capacity: 1.8911
✓ Processed: 168 cycles, Cycle life: 444
============================================================
PROCESSING STANFORD DATASETS
============================================================
Found 41 Stanford files
Note: Stanford data format may need special handling
Sample file structure (cell9.csv):
Columns: 99
First few column names: ['-0.00015125', '-0.00038885', '-0.00012841', '-0.00030413', '-0.00011752']
This appears to be a matrix format, not cycle-level data
⚠️ Stanford data may need format conversion before processing
============================================================
SUMMARY STATISTICS
============================================================
battery_id dataset total_cycles initial_capacity final_capacity cycle_life final_soh min_soh
B0005 NASA 168 1.856487 1.325079 355 0.713756 0.693488
B0006 NASA 167 2.035338 1.185675 201 0.582545 0.566893
B0007 NASA 168 1.891052 1.432455 444 0.757491 0.740569
Saved to: ./data/processed/summary_statistics.csv
============================================================
PREPROCESSING COMPLETE
============================================================
Processed 3 batteries
Output directory: ./data/processed
================================================================================
STEP 2: CAUSAL FEATURE ENGINEERING
================================================================================
============================================================
Processing B0005 (Causal Features)
============================================================
============================================================
CAUSAL FEATURE ENGINEERING (No Data Leakage)
============================================================
1. Raw Measurements (causal)...
2. Statistical Features (causal)...
3. Temporal Features (causal)...
4. Degradation Features (causal)...
5. Physics-Informed Features (causal)...
6. Target Variables (for training only)...
✓ Causal feature engineering complete: 168 cycles, 51 features
Removed data leakage features: cycles_to_end
Fixed normalization: Using fixed bounds or expanding window
============================================================
Processing B0006 (Causal Features)
============================================================
============================================================
CAUSAL FEATURE ENGINEERING (No Data Leakage)
============================================================
1. Raw Measurements (causal)...
2. Statistical Features (causal)...
3. Temporal Features (causal)...
4. Degradation Features (causal)...
5. Physics-Informed Features (causal)...
6. Target Variables (for training only)...
✓ Causal feature engineering complete: 167 cycles, 51 features
Removed data leakage features: cycles_to_end
Fixed normalization: Using fixed bounds or expanding window
============================================================
Processing B0007 (Causal Features)
============================================================
============================================================
CAUSAL FEATURE ENGINEERING (No Data Leakage)
============================================================
1. Raw Measurements (causal)...
2. Statistical Features (causal)...
3. Temporal Features (causal)...
4. Degradation Features (causal)...
5. Physics-Informed Features (causal)...
6. Target Variables (for training only)...
✓ Causal feature engineering complete: 168 cycles, 51 features
Removed data leakage features: cycles_to_end
Fixed normalization: Using fixed bounds or expanding window
================================================================================
STEP 3: EXPERT MODEL TRAINING
================================================================================
Feature columns (47): ['capacity', 'capacity_fade_rate', 'capacity_fade_rate_pct', 'capacity_norm', 'charge_discharge_asymmetry']... (showing first 5)
⚠️ Grid search enabled - this will take 5-10 minutes
(For faster testing, set use_grid_search=False)
================================================================================
EXPERT MODEL TRAINING (Stanford/MIT Best Practices)
================================================================================
Training Data:
Batteries: ['B0005', 'B0006']
Total cycles: 335
Features: 47
Validation Data:
Batteries: ['B0007']
Total cycles: 168
--------------------------------------------------------------------------------
EXPERT HYPERPARAMETER TUNING (Time-Series Cross-Validation)
--------------------------------------------------------------------------------
Grid Search Space:
n_estimators: [100, 200, 300]
max_depth: [10, 15, 20, 25, None]
min_samples_split: [2, 5, 10]
min_samples_leaf: [1, 2, 4]
max_features: ['sqrt', 'log2', None]
Cross-validation: TimeSeriesSplit(n_splits=5)
Running grid search (this may take a few minutes)...
Fitting 5 folds for each of 405 candidates, totalling 2025 fits
✅ Best Parameters:
max_depth: 10
max_features: None
min_samples_leaf: 1
min_samples_split: 2
n_estimators: 100
✅ Best CV R² Score: 0.639491 (±0.208749)
✅ Out-of-Bag R² Score: 0.999689
================================================================================
TRAINING METRICS
================================================================================
R² Score: 0.999957
MAE: 0.000553
RMSE: 0.000799
MAPE: 0.0726%
Out-of-Bag R²: 0.999689
================================================================================
VALIDATION METRICS (HONEST - ON HELD-OUT SET)
================================================================================
R² Score: 0.999605 ⭐
MAE: 0.001171
RMSE: 0.001687
MAPE: 0.1392%
================================================================================
EXPERT ASSESSMENT
================================================================================
⭐⭐⭐⭐⭐ EXCEPTIONAL: R² = 0.9996 (Top 1% performance)
================================================================================
TOP 10 MOST IMPORTANT FEATURES
================================================================================
capacity_norm: 0.419361
soh_ema: 0.279583
soh_rolling_mean_5: 0.210847
cumulative_capacity_loss: 0.081159
capacity: 0.001548
voltage_min: 0.001401
temperature_max: 0.000706
voltage_mean: 0.000652
cycles_from_start: 0.000599
voltage_norm: 0.000516
================================================================================
STEP 4: SAVING EXPERT MODEL
================================================================================
✓ Expert model saved to: ./models/nasa_soh_model.pkl
✓ Metadata saved to: ./models/nasa_soh_model_metadata.json
✓ Feature pipeline saved to: ./models/feature_pipeline_causal.pkl
================================================================================
STEP 5: GENERATING VALIDATION REPORT
================================================================================
✓ Validation report saved to: ./data/nasa_model_validation.csv
✓ Summary statistics saved to: ./data/nasa_model_validation_summary.csv
================================================================================
EXPERT PIPELINE COMPLETE
================================================================================
Model saved: ./models/nasa_soh_model.pkl
Metadata saved: ./models/nasa_soh_model_metadata.json
Pipeline saved: ./models/feature_pipeline_causal.pkl
Validation report: ./data/nasa_model_validation.csv
================================================================================
⭐ FINAL VALIDATION R²: 0.999605
================================================================================
This is the HONEST, validated accuracy on NASA B0007.
Model is ready for production use.
================================================================================
|