get metrics (and add missing update_kwargs() method)

ml.py

@@ -33,7 +33,6 @@ from qgis.core import (Qgis,
 import torch
 import torch.nn as nn
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score
 from sklearn.preprocessing import LabelEncoder
 from sklearn.model_selection import train_test_split
 
@@ -49,6 +48,21 @@ from .utils.algo import (
 import sklearn.ensemble as ensemble
 import sklearn.neighbors as neighbors
 from sklearn.base import ClassifierMixin, RegressorMixin
+from sklearn.metrics import (
+                            accuracy_score,
+                            precision_score, 
+                            recall_score, 
+                            f1_score, 
+                            confusion_matrix, 
+                            classification_report
+                            )
+from sklearn.metrics import (
+                            mean_absolute_error, 
+                            mean_squared_error, 
+                            r2_score,
+                            )
+
+
 def check_model_type(model):
     if isinstance(model, ClassifierMixin):
         return "classification"
@@ -269,6 +283,54 @@ class MLAlgorithm(SHPAlgorithm):
         except AttributeError:
             self.model = instantiate_sklearn_algorithm(neighbors, self.method_name, **kwargs)
 
+
+
+    def update_kwargs(self, kwargs_dict):
+
+        for key, value in self.passed_kwargs.items():
+            if key in kwargs_dict.keys():
+                kwargs_dict[key] = value
+
+        return kwargs_dict
+
+
+    def get_metrics(self, test_gts, predictions, feedback):
+
+        task_type = check_model_type(self.model)
+
+        if task_type == 'classification':
+            # Evaluate the model
+            accuracy = accuracy_score(test_gts, predictions)
+            precision = precision_score(test_gts, predictions, average='weighted')  # Modify `average` for multiclass if necessary
+            recall = recall_score(test_gts, predictions, average='weighted')
+            f1 = f1_score(test_gts, predictions, average='weighted')
+            conf_matrix = confusion_matrix(test_gts, predictions)
+            class_report = classification_report(test_gts, predictions)
+
+
+            feedback.pushInfo(f'Accuracy:\t {accuracy}')
+            feedback.pushInfo(f'Precision:\t {precision}')
+            feedback.pushInfo(f'Recall:\t {recall}')
+            feedback.pushInfo(f'F1-Score:\t {f1}')
+            feedback.pushInfo(f'Confusion Matrix:\n {conf_matrix}')
+            feedback.pushInfo(f'Classification Report:\n {class_report}')
+
+        elif task_type == 'regression':
+            pass
+            mae = mean_absolute_error(test_gts, predictions)
+            mse = mean_squared_error(test_gts, predictions)
+            rmse = np.sqrt(mse)
+            r2 = r2_score(test_gts, predictions)
+
+            feedback.pushInfo(f'MAE:\t {mae}')
+            feedback.pushInfo(f'MSE:\t {mse}')
+            feedback.pushInfo(f'RMSE:\t {rmse}')
+            feedback.pushInfo(f'R2 Score:\t {r2}')
+        
+        else:
+            feedback.pushWarning('Unable to evaluate the model !!')
+
+
     def get_algorithms(self):
         required_methods = ['fit', 'predict']
         ensemble_algos = get_sklearn_algorithms_with_methods(ensemble, required_methods)