debug: return debug file when all forecasts fail

src/forecasting/chronos_inference.py

@@ -233,18 +233,33 @@ class ChronosInferencePipeline:
 
         # Build DataFrame
         data = {'timestamp': timestamps}
+        
+        successful_borders = []
+        failed_borders = []
 
         for border, forecast_data in results['borders'].items():
             if 'error' not in forecast_data:
                 data[f'{border}_median'] = forecast_data['median']
                 data[f'{border}_q10'] = forecast_data['q10']
                 data[f'{border}_q90'] = forecast_data['q90']
-
+                successful_borders.append(border)
+            else:
+                failed_borders.append((border, forecast_data['error']))
+
+        # Log results
+        print(f"[EXPORT] Forecast export summary:", flush=True)
+        print(f"  Successful: {len(successful_borders)} borders", flush=True)
+        print(f"  Failed: {len(failed_borders)} borders", flush=True)
+        if failed_borders:
+            print(f"[EXPORT] Errors:", flush=True)
+            for border, error in failed_borders:
+                print(f"  {border}: {error}", flush=True)
+        
         df = pl.DataFrame(data)
         df.write_parquet(output_path)
 
-        print(f"✓ Exported to: {output_path}")
-        print(f"  Shape: {df.shape}")
+        print(f"[EXPORT] Exported to: {output_path}", flush=True)
+        print(f"[EXPORT] Shape: {df.shape}, Columns: {len(df.columns)}", flush=True)
 
         return output_path
 
@@ -289,18 +304,43 @@ def run_inference(
         forecast_days=forecast_days
     )
 
-    # Diagnostic logging
-    print(f"[DEBUG] Results summary:", flush=True)
-    print(f"  Borders in results: {list(results['borders'].keys())}", flush=True)
-    for border, data in results['borders'].items():
-        if 'error' in data:
-            print(f"  {border}: ERROR - {data['error']}", flush=True)
-        else:
-            print(f"  {border}: OK (median values count: {len(data.get('median', []))})", flush=True)
+    # Write debug file
+    debug_filename = f"debug_{run_date}_{forecast_type}.txt"
+    debug_path = os.path.join(output_dir, debug_filename)
+    with open(debug_path, 'w') as f:
+        f.write(f"Results summary:
+")
+        f.write(f"  Run date: {results['run_date']}
+")
+        f.write(f"  Forecast days: {results['forecast_days']}
+")
+        f.write(f"  Borders in results: {list(results['borders'].keys())}
+
+")
+        for border, data in results['borders'].items():
+            if 'error' in data:
+                f.write(f"  {border}: ERROR - {data['error']}
+")
+            else:
+                f.write(f"  {border}: OK
+")
+                f.write(f"    median count: {len(data.get('median', []))}
+")
+                f.write(f"    q10 count: {len(data.get('q10', []))}
+")
+                f.write(f"    q90 count: {len(data.get('q90', []))}
+")
+    print(f"Debug file written to: {debug_path}", flush=True)
     
     # Export to parquet
     output_filename = f"forecast_{run_date}_{forecast_type}.parquet"
     output_path = os.path.join(output_dir, output_filename)
     pipeline.export_to_parquet(results, output_path)
-
+    
+    # Check if forecast has data, if not return debug file
+    successful_count = sum(1 for data in results['borders'].values() if 'error' not in data)
+    if successful_count == 0:
+        print(f"[WARNING] No successful forecasts! Returning debug file instead.", flush=True)
+        return debug_path
+    
     return output_path