Updates

ETB-API/analytics_predictive_insights/management/commands/train_predictive_models.py

@@ -0,0 +1,108 @@
+"""
+Management command to train predictive models
+"""
+from django.core.management.base import BaseCommand, CommandError
+from analytics_predictive_insights.models import PredictiveModel
+from analytics_predictive_insights.ml.predictive_models import PredictiveModelService
+
+
+class Command(BaseCommand):
+    """Train predictive models"""
+    
+    help = 'Train predictive models that are in training status'
+    
+    def add_arguments(self, parser):
+        parser.add_argument(
+            '--model-id',
+            type=str,
+            help='Train a specific model ID only'
+        )
+        parser.add_argument(
+            '--force',
+            action='store_true',
+            help='Force retraining of active models'
+        )
+    
+    def handle(self, *args, **options):
+        """Handle the command execution"""
+        model_id = options.get('model_id')
+        force = options.get('force', False)
+        
+        try:
+            # Initialize predictive model service
+            model_service = PredictiveModelService()
+            
+            # Get models to train
+            if model_id:
+                models = PredictiveModel.objects.filter(id=model_id)
+                if not models.exists():
+                    raise CommandError(f'No model found with ID: {model_id}')
+            else:
+                if force:
+                    models = PredictiveModel.objects.filter(
+                        model_type__in=[
+                            'INCIDENT_PREDICTION',
+                            'SEVERITY_PREDICTION',
+                            'RESOLUTION_TIME_PREDICTION',
+                            'COST_PREDICTION'
+                        ]
+                    )
+                else:
+                    models = PredictiveModel.objects.filter(status='TRAINING')
+            
+            self.stdout.write(f'Training {models.count()} models...')
+            
+            total_trained = 0
+            total_failed = 0
+            
+            for model in models:
+                try:
+                    self.stdout.write(f'Training model: {model.name}...')
+                    
+                    result = model_service.train_model(str(model.id))
+                    
+                    if result['success']:
+                        total_trained += 1
+                        self.stdout.write(
+                            self.style.SUCCESS(f'✓ Successfully trained {model.name}')
+                        )
+                        
+                        # Display metrics
+                        if 'metrics' in result:
+                            metrics = result['metrics']
+                            self.stdout.write(f'  Accuracy: {metrics.get("accuracy", "N/A")}')
+                            self.stdout.write(f'  Precision: {metrics.get("precision", "N/A")}')
+                            self.stdout.write(f'  Recall: {metrics.get("recall", "N/A")}')
+                            self.stdout.write(f'  F1 Score: {metrics.get("f1_score", "N/A")}')
+                            self.stdout.write(f'  R2 Score: {metrics.get("r2_score", "N/A")}')
+                        
+                        self.stdout.write(f'  Training samples: {result.get("training_samples", "N/A")}')
+                        self.stdout.write(f'  Training duration: {result.get("training_duration", "N/A")} seconds')
+                        
+                    else:
+                        total_failed += 1
+                        self.stdout.write(
+                            self.style.ERROR(f'✗ Failed to train {model.name}: {result.get("error", "Unknown error")}')
+                        )
+                        
+                except Exception as e:
+                    total_failed += 1
+                    self.stdout.write(
+                        self.style.ERROR(f'✗ Error training {model.name}: {str(e)}')
+                    )
+            
+            self.stdout.write('\nTraining Summary:')
+            self.stdout.write(f'  Successfully trained: {total_trained}')
+            self.stdout.write(f'  Failed: {total_failed}')
+            
+            if total_trained > 0:
+                self.stdout.write(
+                    self.style.SUCCESS(f'✓ Training completed successfully')
+                )
+            else:
+                self.stdout.write(
+                    self.style.WARNING('⚠ No models were successfully trained')
+                )
+            
+        except Exception as e:
+            raise CommandError(f'Error executing command: {str(e)}')