Addding save,load and modifying fit

sklearn_genetic/genetic_search.py

@@ -30,6 +30,10 @@
 from .utils.random import weighted_bool_individual
 from .utils.tools import cxUniform, mutFlipBit, novelty_scorer
 
+import pickle
+import os
+from .callbacks.model_checkpoint import ModelCheckpoint
+
 
 class GASearchCV(BaseSearchCV):
     """
@@ -524,6 +528,16 @@ def fit(self, X, y, callbacks=None):
         # Make sure the callbacks are valid
         self.callbacks = check_callback(callbacks)
 
+        # Load state if a checkpoint exists
+        for callback in self.callbacks:
+            if isinstance(callback, ModelCheckpoint):
+                if os.path.exists(callback.checkpoint_path): 
+                    checkpoint_data = callback.load()
+                    if checkpoint_data:
+                        self.estimator.__dict__.update(checkpoint_data['estimator_state'])  # noqa
+                        self.logbook = checkpoint_data['logbook']
+                    break
+
         if callable(self.scoring):
             self.scorer_ = self.scoring
             self.metrics_list = [self.refit_metric]
@@ -601,6 +615,28 @@ def fit(self, X, y, callbacks=None):
 
         return self
 
+    def save(self, filepath):
+        """Save the current state of the GASearchCV instance to a file."""
+        try:
+            checkpoint_data = self.__dict__
+            with open(filepath, 'wb') as f:
+                pickle.dump(checkpoint_data, f)
+            print(f"GASearchCV model successfully saved to {filepath}")
+        except Exception as e:
+            print(f"Error saving GASearchCV: {e}")
+
+    @staticmethod
+    def load(filepath):
+        """Load a GASearchCV instance from a file."""
+        try:
+            with open(filepath, 'rb') as f:
+                checkpoint_data = pickle.load(f)
+                model = GASearchCV(**checkpoint_data)
+            print(f"GASearchCV model successfully loaded from {filepath}")
+            return model
+        except Exception as e:
+            print(f"Error loading GASearchCV: {e}")
+
     def _select_algorithm(self, pop, stats, hof):
         """
         It selects the algorithm to run from the sklearn_genetic.algorithms module
@@ -1131,6 +1167,16 @@ def fit(self, X, y, callbacks=None):
         # Make sure the callbacks are valid
         self.callbacks = check_callback(callbacks)
 
+        # Load state if a checkpoint exists
+        for callback in self.callbacks:
+            if isinstance(callback, ModelCheckpoint):
+                if os.path.exists(callback.checkpoint_path): 
+                    checkpoint_data = callback.load()
+                    if checkpoint_data:
+                        self.estimator.__dict__.update(checkpoint_data['estimator_state'])  # noqa
+                        self.logbook = checkpoint_data['logbook']
+                    break
+
         if callable(self.scoring):
             self.scorer_ = self.scoring
             self.metrics_list = [self.refit_metric]
@@ -1192,6 +1238,28 @@ def fit(self, X, y, callbacks=None):
 
         return self
 
+    def save(self, filepath):
+        """Save the current state of the GAFeatureSelectionCV instance to a file."""
+        try:
+            checkpoint_data = self.__dict__ 
+            with open(filepath, 'wb') as f:
+                pickle.dump(checkpoint_data, f)
+            print(f"GAFeatureSelectionCV model successfully saved to {filepath}")
+        except Exception as e:
+            print(f"Error saving GAFeatureSelectionCV: {e}")
+
+    @staticmethod
+    def load(filepath):
+        """Load a GAFeatureSelectionCV instance from a file."""
+        try:
+            with open(filepath, 'rb') as f:
+                checkpoint_data = pickle.load(f)
+                model = GAFeatureSelectionCV(**checkpoint_data)
+            print(f"GAFeatureSelectionCV model successfully loaded from {filepath}") # noqa
+            return model
+        except Exception as e:
+            print(f"Error loading GAFeatureSelectionCV: {e}")
+
     def _select_algorithm(self, pop, stats, hof):
         """
         It selects the algorithm to run from the sklearn_genetic.algorithms module