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| """ | |
| Causal Feature Engineering for Battery SOH Prediction | |
| FIXED VERSION: All features use only data ≤ current cycle | |
| Removes data leakage from: | |
| - cycles_to_end (used max cycle - future knowledge) | |
| - Normalization (used all cycles - future knowledge) | |
| - charge_discharge_asymmetry (used all cycles) | |
| """ | |
| import pandas as pd | |
| import numpy as np | |
| from sklearn.preprocessing import MinMaxScaler | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| def calculate_energy_throughput(df): | |
| """Calculate energy throughput: ∫(voltage * current) dt per cycle.""" | |
| if 'time' not in df.columns: | |
| df = df.copy() | |
| df['time'] = df.groupby('cycle_index').cumcount() | |
| df['power'] = df['voltage'] * df['current'] | |
| energy_per_cycle = df.groupby('cycle_index').apply( | |
| lambda x: np.trapz(x['power'], x['time']) if len(x) > 1 else 0 | |
| ) | |
| return energy_per_cycle | |
| def engineer_features_causal(df, initial_capacity, use_fixed_bounds=True): | |
| """ | |
| Engineer CAUSAL features - uses only data ≤ current cycle. | |
| FIXES: | |
| 1. Removed cycles_to_end (data leakage) | |
| 2. Fixed normalization to use only past cycles or fixed bounds | |
| 3. Fixed charge_discharge_asymmetry to use only past cycles | |
| 4. Rolling windows verified backward-only (center=False) | |
| Parameters: | |
| ----------- | |
| df : DataFrame | |
| Preprocessed DataFrame with cycle_index, voltage, current, temperature, capacity, soh | |
| initial_capacity : float | |
| Initial capacity value | |
| use_fixed_bounds : bool | |
| If True, use fixed physical bounds for normalization. If False, use expanding window. | |
| Returns: | |
| -------- | |
| DataFrame: Feature matrix with causal features only | |
| """ | |
| print("\n" + "="*60) | |
| print("CAUSAL FEATURE ENGINEERING (No Data Leakage)") | |
| print("="*60) | |
| df_feat = df.copy() | |
| if 'cycle_index' not in df_feat.columns: | |
| print("Error: cycle_index column not found") | |
| return None | |
| # Sort by cycle_index | |
| df_feat = df_feat.sort_values('cycle_index').reset_index(drop=True) | |
| cycle_groups = df_feat.groupby('cycle_index') | |
| features = pd.DataFrame() | |
| features['cycle_index'] = cycle_groups['cycle_index'].first() | |
| # ======================================================================== | |
| # 1. RAW MEASUREMENTS (causal - current cycle only) | |
| # ======================================================================== | |
| print("\n1. Raw Measurements (causal)...") | |
| if 'voltage' in df_feat.columns: | |
| features['voltage_mean'] = cycle_groups['voltage'].mean() | |
| # FIX: Use fixed bounds (0-5V typical) or expanding window | |
| if use_fixed_bounds: | |
| features['voltage_norm'] = (features['voltage_mean'] - 0) / (5.0 - 0) # Fixed 0-5V range | |
| else: | |
| # Expanding window: use only past cycles | |
| features['voltage_norm'] = features['voltage_mean'].expanding().apply( | |
| lambda x: (x.iloc[-1] - x.min()) / (x.max() - x.min()) if x.max() > x.min() else 0.5 | |
| ) | |
| if 'current' in df_feat.columns: | |
| features['current_mean'] = cycle_groups['current'].mean() | |
| # FIX: Use fixed bounds or expanding window | |
| if use_fixed_bounds: | |
| # Typical range: -10A to +10A (adjust based on battery specs) | |
| features['current_norm'] = (features['current_mean'] + 10) / 20 | |
| else: | |
| features['current_norm'] = features['current_mean'].expanding().apply( | |
| lambda x: (x.iloc[-1] - x.min()) / (x.max() - x.min()) if x.max() > x.min() else 0.5 | |
| ) | |
| features['is_charge'] = (features['current_mean'] > 0).astype(int) | |
| features['is_discharge'] = (features['current_mean'] < 0).astype(int) | |
| if 'temperature' in df_feat.columns: | |
| features['temperature_mean'] = cycle_groups['temperature'].mean() | |
| # FIX: Use fixed bounds (0-50°C typical) or expanding window | |
| if use_fixed_bounds: | |
| features['temperature_norm'] = (features['temperature_mean'] - 0) / (50.0 - 0) | |
| else: | |
| features['temperature_norm'] = features['temperature_mean'].expanding().apply( | |
| lambda x: (x.iloc[-1] - x.min()) / (x.max() - x.min()) if x.max() > x.min() else 0.5 | |
| ) | |
| if 'capacity' in df_feat.columns: | |
| features['capacity'] = cycle_groups['capacity'].mean() | |
| features['capacity_norm'] = features['capacity'] / initial_capacity if initial_capacity > 0 else features['capacity'] | |
| if 'soh' in df_feat.columns: | |
| features['soh'] = cycle_groups['soh'].mean() | |
| # ======================================================================== | |
| # 2. STATISTICAL FEATURES (causal - current cycle only) | |
| # ======================================================================== | |
| print("2. Statistical Features (causal)...") | |
| for col in ['voltage', 'current', 'temperature']: | |
| if col in df_feat.columns: | |
| features[f'{col}_mean'] = cycle_groups[col].mean() | |
| features[f'{col}_std'] = cycle_groups[col].std().fillna(0) | |
| features[f'{col}_min'] = cycle_groups[col].min() | |
| features[f'{col}_max'] = cycle_groups[col].max() | |
| features[f'{col}_range'] = features[f'{col}_max'] - features[f'{col}_min'] | |
| features[f'{col}_median'] = cycle_groups[col].median() | |
| if 'current' in df_feat.columns and 'capacity' in df_feat.columns: | |
| charge_capacity = df_feat[df_feat['current'] > 0].groupby('cycle_index')['capacity'].mean() | |
| discharge_capacity = df_feat[df_feat['current'] < 0].groupby('cycle_index')['capacity'].mean() | |
| features['charge_discharge_diff'] = (charge_capacity - discharge_capacity).fillna(0) | |
| if 'voltage' in df_feat.columns and 'current' in df_feat.columns: | |
| try: | |
| energy = calculate_energy_throughput(df_feat) | |
| features['energy_throughput'] = features['cycle_index'].map(energy).fillna(0) | |
| except: | |
| features['power_mean'] = features['voltage_mean'] * features['current_mean'] | |
| features['energy_throughput'] = features['power_mean'].abs() | |
| # ======================================================================== | |
| # 3. TEMPORAL FEATURES (causal - removed cycles_to_end) | |
| # ======================================================================== | |
| print("3. Temporal Features (causal)...") | |
| features['cycles_from_start'] = features['cycle_index'] - features['cycle_index'].min() | |
| # REMOVED: cycles_to_end (data leakage - uses max cycle) | |
| if 'time' in df_feat.columns: | |
| features['cumulative_time'] = cycle_groups['time'].sum() | |
| features['time_per_cycle'] = cycle_groups['time'].mean() | |
| else: | |
| features['cumulative_time'] = features['cycles_from_start'] | |
| features['time_per_cycle'] = 1.0 | |
| features['days_since_start'] = features['cycles_from_start'] * 0.5 | |
| # ======================================================================== | |
| # 4. DEGRADATION FEATURES (causal - backward-looking only) | |
| # ======================================================================== | |
| print("4. Degradation Features (causal)...") | |
| if 'soh' in features.columns: | |
| # FIXED: Backward-looking diff (uses only past) | |
| features['soh_fade_rate'] = features['soh'].diff().fillna(0) | |
| features['soh_fade_rate_abs'] = features['soh_fade_rate'].abs() | |
| # FIXED: Rolling windows with center=False (backward-only) | |
| features['soh_rolling_mean_5'] = features['soh'].rolling(window=5, min_periods=1, center=False).mean() | |
| features['soh_rolling_std_5'] = features['soh'].rolling(window=5, min_periods=1, center=False).std().fillna(0) | |
| features['soh_ema'] = features['soh'].ewm(span=5, adjust=False).mean() | |
| features['soh_second_derivative'] = features['soh_fade_rate'].diff().fillna(0) | |
| features['cumulative_capacity_loss'] = (1 - features['soh']) * initial_capacity if initial_capacity > 0 else (1 - features['soh']) | |
| if 'capacity' in features.columns: | |
| features['capacity_fade_rate'] = features['capacity'].diff().fillna(0) | |
| features['capacity_fade_rate_pct'] = features['capacity'].pct_change().fillna(0) | |
| # ======================================================================== | |
| # 5. PHYSICS-INFORMED FEATURES (causal) | |
| # ======================================================================== | |
| print("5. Physics-Informed Features (causal)...") | |
| # FIXED: charge_discharge_asymmetry - use only current cycle ratio, not cumulative | |
| if 'current' in df_feat.columns: | |
| # Calculate per-cycle ratio instead of cumulative | |
| charge_by_cycle = df_feat[df_feat['current'] > 0].groupby('cycle_index')['current'].sum() | |
| discharge_by_cycle = df_feat[df_feat['current'] < 0].groupby('cycle_index')['current'].sum().abs() | |
| features['charge_discharge_asymmetry'] = (charge_by_cycle / discharge_by_cycle).fillna(1.0) | |
| features['charge_discharge_asymmetry'] = features['cycle_index'].map(features['charge_discharge_asymmetry']).fillna(1.0) | |
| if 'temperature' in df_feat.columns: | |
| temp_stress = cycle_groups['temperature'].apply(lambda x: (x > 40).sum()) | |
| features['temperature_stress'] = temp_stress / cycle_groups['temperature'].count() | |
| features['max_temperature'] = cycle_groups['temperature'].max() | |
| features['min_temperature'] = cycle_groups['temperature'].min() | |
| features['temperature_range'] = features['max_temperature'] - features['min_temperature'] | |
| if 'voltage' in df_feat.columns and 'current' in df_feat.columns: | |
| discharge_voltage_var = df_feat[df_feat['current'] < 0].groupby('cycle_index')['voltage'].var().fillna(0) | |
| features['voltage_variance_discharge'] = features['cycle_index'].map(discharge_voltage_var).fillna(0) | |
| if 'voltage_mean' in features.columns and 'current_mean' in features.columns: | |
| if 'power_mean' not in features.columns: | |
| features['power_mean'] = features['voltage_mean'] * features['current_mean'] | |
| features['power_abs'] = features['power_mean'].abs() | |
| # ======================================================================== | |
| # 6. TARGET VARIABLES (for training only - not used in production) | |
| # ======================================================================== | |
| print("6. Target Variables (for training only)...") | |
| if 'soh' in features.columns: | |
| # These are TARGETS, not features - only used during training | |
| features['soh_10_cycles_ahead'] = features['soh'].shift(-10).fillna(features['soh'].iloc[-1]) | |
| eol_threshold = 0.80 | |
| features['remaining_cycles_to_eol'] = None | |
| for idx, row in features.iterrows(): | |
| future_soh = features[features['cycle_index'] > row['cycle_index']]['soh'] | |
| eol_cycles = future_soh[future_soh < eol_threshold] | |
| if len(eol_cycles) > 0: | |
| eol_cycle = eol_cycles.index[0] | |
| features.loc[idx, 'remaining_cycles_to_eol'] = features.loc[eol_cycle, 'cycle_index'] - row['cycle_index'] | |
| else: | |
| # Estimate based on degradation rate if EOL not reached | |
| current_degradation = row['soh_fade_rate'] if 'soh_fade_rate' in row else 0 | |
| if current_degradation < 0: | |
| cycles_to_eol = (row['soh'] - eol_threshold) / abs(current_degradation) | |
| features.loc[idx, 'remaining_cycles_to_eol'] = max(0, cycles_to_eol) | |
| else: | |
| features.loc[idx, 'remaining_cycles_to_eol'] = 1000 # Large number if not degrading | |
| features['remaining_cycles_to_eol'] = features['remaining_cycles_to_eol'].fillna(0) | |
| # Fill any remaining NaN values | |
| features = features.fillna(0) | |
| print(f"\n✓ Causal feature engineering complete: {len(features)} cycles, {len(features.columns)} features") | |
| print(f" Removed data leakage features: cycles_to_end") | |
| print(f" Fixed normalization: Using fixed bounds or expanding window") | |
| return features | |
| def engineer_features_for_all_batteries_causal(dataframes_dict, initial_capacities_dict, use_fixed_bounds=True): | |
| """Engineer causal features for all batteries.""" | |
| all_features = {} | |
| for battery_id, df in dataframes_dict.items(): | |
| print(f"\n{'='*60}") | |
| print(f"Processing {battery_id} (Causal Features)") | |
| print(f"{'='*60}") | |
| initial_capacity = initial_capacities_dict.get(battery_id, df['capacity'].max() if 'capacity' in df.columns else 1.0) | |
| features = engineer_features_causal(df, initial_capacity, use_fixed_bounds) | |
| if features is not None: | |
| all_features[battery_id] = features | |
| return all_features | |
| if __name__ == "__main__": | |
| import sys | |
| sys.path.append('.') | |
| from unified_data_pipeline import preprocess_all_datasets | |
| dataframes, summary = preprocess_all_datasets() | |
| if dataframes: | |
| initial_capacities = {} | |
| for battery_id, df in dataframes.items(): | |
| initial_capacities[battery_id] = df['capacity'].max() if 'capacity' in df.columns else 1.0 | |
| all_features = engineer_features_for_all_batteries_causal(dataframes, initial_capacities) | |
| import os | |
| os.makedirs('./data/features_causal', exist_ok=True) | |
| for battery_id, features in all_features.items(): | |
| output_path = f'./data/features_causal/{battery_id}_features_causal.csv' | |
| features.to_csv(output_path, index=False) | |
| print(f"\nSaved causal features to: {output_path}") | |