creating new callback

sklearn_genetic/callbacks/model_checkpoint.py

@@ -0,0 +1,49 @@
+import pickle
+from .base import BaseCallback
+from .loggers import LogbookSaver
+from copy import deepcopy
+
+
+class ModelCheckpoint(BaseCallback):
+    def __init__(self,  checkpoint_path, **dump_options):
+        self.checkpoint_path = checkpoint_path
+        self.dump_options = dump_options
+
+    def on_step(self, record=None, logbook=None, estimator=None):
+        try:
+            if logbook is not None and len(logbook) > 0:
+                logbook_saver = LogbookSaver(self.checkpoint_path, **self.dump_options)  # noqa
+                logbook_saver.on_step(record, logbook, estimator)
+
+            estimator_state = {
+                'estimator': estimator.estimator,
+                'cv': estimator.cv,
+                'scoring': estimator.scoring,
+                'population_size': estimator.population_size,
+                'generations': estimator.generations,
+                'crossover_probability': estimator.crossover_probability,
+                'mutation_probability': estimator.mutation_probability,
+                'param_grid': estimator.param_grid,
+                'algorithm': estimator.algorithm,
+                'param_grid': estimator.param_grid,
+            }
+            checkpoint_data = {
+                'estimator_state': estimator_state,
+                'logbook': deepcopy(logbook)
+            }
+            with open(self.checkpoint_path, 'wb') as f:
+                pickle.dump(checkpoint_data, f)
+                print(f"Checkpoint save in {self.checkpoint_path}")
+
+        except Exception as e:
+            print(f"Error saving checkpoint: {e}")
+
+    def load(self):
+        """Load the model state from the checkpoint file."""
+        try:
+            with open(self.checkpoint_path, 'rb') as f:
+                checkpoint_data = pickle.load(f)
+                return checkpoint_data
+        except Exception as e:
+            print(f"Error loading checkpoint: {e}")
+            return None