harvest pred working better now

python_app/22_harvest_baseline_prediction.py

@@ -46,6 +46,7 @@ from pathlib import Path
 from harvest_date_pred_utils import (
     load_model_and_config,
     extract_features,
+    run_phase1_growing_window,
     run_two_step_refinement,
     build_production_harvest_table
 )

python_app/harvest_date_pred_utils.py

@@ -307,45 +307,32 @@ def run_phase1_growing_window(field_data, model, config, scalers, ci_column, dev
     
     For each detected harvest, reset DOY counter for the next season.
     This allows the model to detect multiple consecutive harvests in multi-year data.
-    
-    Algorithm:
-    1. Start with season_anchor_day = 0 (DOY 1 at day 0)
-    2. Expand window: [0:1], [0:2], [0:3], ... until threshold crossed
-    3. When harvest detected: record date, set new season_anchor = day after harvest
-    4. Continue from next season start
-    
-    Args:
-        threshold (float): Probability threshold (default 0.45)
-        consecutive_days (int): Required consecutive days above threshold (default 2)
-    
-    Returns list of (harvest_date, harvest_idx) tuples.
     """
     harvest_dates = []
-    season_anchor_day = 0  # DOY 1 starts at day 0
+    season_anchor_day = 0
     current_pos = 0
     
     while current_pos < len(field_data):
         consecutive_above_threshold = 0
-        min_window_size = 120  # Need at least 120 days (~4 months) for patterns to establish
+        min_window_size = 120
         
         for window_end in range(current_pos + 1, len(field_data) + 1):
             window_data = field_data.iloc[current_pos:window_end].copy().reset_index(drop=True)
             
-            # Skip if window is too small (model needs long sequences for pattern learning)
             if len(window_data) < min_window_size:
                 continue
             
             try:
-                # CRITICAL: Pass season_anchor_day so DOY resets after harvest
+                reset_doy = current_pos > season_anchor_day
+                
                 features = extract_features(
                     window_data, 
                     config['features'], 
                     ci_column=ci_column,
-                    season_anchor_day=season_anchor_day,
+                    season_anchor_day=season_anchor_day if reset_doy else None,
                     lookback_start=current_pos
                 )
                 
-                # Apply scalers
                 features_scaled = features.copy().astype(float)
                 for fi, scaler in enumerate(scalers):
                     try:
@@ -353,12 +340,10 @@ def run_phase1_growing_window(field_data, model, config, scalers, ci_column, dev
                     except Exception:
                         pass
                 
-                # Run model on expanding window
                 with torch.no_grad():
                     x_tensor = torch.tensor(features_scaled, dtype=torch.float32).unsqueeze(0).to(device)
                     imminent_probs, detected_probs = model(x_tensor)
                 
-                # Check LAST timestep only
                 last_prob = detected_probs[0, -1].item()
                 
                 if last_prob > threshold:
@@ -366,23 +351,18 @@ def run_phase1_growing_window(field_data, model, config, scalers, ci_column, dev
                 else:
                     consecutive_above_threshold = 0
                 
-                # Harvest detected: N consecutive days above threshold
                 if consecutive_above_threshold >= consecutive_days:
                     harvest_idx = current_pos + window_end - consecutive_days
                     harvest_date = field_data.iloc[harvest_idx]['Date']
-                    harvest_dates.append((harvest_date, harvest_idx))
                     
-                    # CRITICAL: Reset season anchor for next season
+                    harvest_dates.append((harvest_date, harvest_idx))
-                    # DOY 1 starts at day after harvest
                     season_anchor_day = harvest_idx + 1
                     current_pos = harvest_idx + 1
                     break
                     
             except Exception as e:
-                # Skip window on error
                 continue
         else:
-            # No more harvests found
             break
     
     return harvest_dates
@@ -413,8 +393,21 @@ def run_phase2_refinement(field_data, phase1_harvests, model, config, scalers, c
             window_start_date = season_start_date - pd.Timedelta(days=40)
             window_end_date = phase1_harvest_date + pd.Timedelta(days=40)
             
-            window_start_idx = max(0, (field_data['Date'] >= window_start_date).idxmax() if (field_data['Date'] >= window_start_date).any() else 0)
+            # FIXED: Use proper index selection
-            window_end_idx = min(len(field_data), (field_data['Date'] <= window_end_date).idxmax() + 1 if (field_data['Date'] <= window_end_date).any() else len(field_data))
+            mask_start = field_data['Date'] >= window_start_date
+            mask_end = field_data['Date'] <= window_end_date
+            
+            if mask_start.any():
+                window_start_idx = mask_start.idxmax()  # First True index
+            else:
+                window_start_idx = 0
+            
+            if mask_end.any():
+                # Last True index: find where condition becomes False from the right
+                true_indices = np.where(mask_end)[0]
+                window_end_idx = true_indices[-1] + 1  # +1 for slicing (exclusive end)
+            else:
+                window_end_idx = len(field_data)
             
             if window_end_idx <= window_start_idx:
                 refined_harvests.append((phase1_harvest_date, phase1_idx))
@@ -525,6 +518,10 @@ def run_two_step_refinement(df: pd.DataFrame, model, config, scalers, device=Non
     
     print()  # New line after progress bar
     print(f"  ✓ Complete: Found {harvests_found} harvest events across {total_fields} fields")
+    if results:
+        print(f"    Sample harvest dates: {results[0]['phase2_harvest_date']}")
+        if len(results) > 1:
+            print(f"                        {results[-1]['phase2_harvest_date']}")
     
     return results