[ENH] Make deep clustering consisting with other deep learning submod…

…ules in terms of saving/loading (#2359)

* add init save and load test

* fix bug test

* call save last model

aeon/clustering/deep_learning/_ae_abgru.py

@@ -73,6 +73,8 @@ class AEAttentionBiGRUClusterer(BaseDeepClusterer):
         Whether or not to save the last model, last
         epoch trained, using the base class method
         save_last_model_to_file.
+    save_init_model : bool, default = False
+        Whether to save the initialization of the  model.
     best_file_name : str, default = "best_model"
         The name of the file of the best model, if
         save_best_model is set to False, this parameter
@@ -81,6 +83,10 @@ class AEAttentionBiGRUClusterer(BaseDeepClusterer):
         The name of the file of the last model, if
         save_last_model is set to False, this parameter
         is discarded.
+    init_file_name : str, default = "init_model"
+        The name of the file of the init model, if
+        save_init_model is set to False,
+        this parameter is discarded.
     callbacks : keras.callbacks, default = None
         List of keras callbacks.
 
@@ -120,8 +126,10 @@ def __init__(
         file_path="./",
         save_best_model=False,
         save_last_model=False,
+        save_init_model=False,
         best_file_name="best_model",
-        last_file_name="last_file",
+        last_file_name="last_model",
+        init_file_name="init_model",
         callbacks=None,
     ):
         self.latent_space_dim = latent_space_dim
@@ -139,7 +147,9 @@ def __init__(
         self.n_epochs = n_epochs
         self.save_best_model = save_best_model
         self.save_last_model = save_last_model
+        self.save_init_model = save_init_model
         self.best_file_name = best_file_name
+        self.init_file_name = init_file_name
         self.random_state = random_state
 
         super().__init__(
@@ -231,6 +241,9 @@ def _fit(self, X):
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape)
 
+        if self.save_init_model:
+            self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
         if self.verbose:
             self.training_model_.summary()
 
@@ -282,6 +295,9 @@ def _fit(self, X):
 
         self._fit_clustering(X=X)
 
+        if self.save_last_model:
+            self.save_last_model_to_file(file_path=self.file_path)
+
         gc.collect()
 
         return self

aeon/clustering/deep_learning/_ae_bgru.py

@@ -71,6 +71,8 @@ class AEBiGRUClusterer(BaseDeepClusterer):
         Whether or not to save the last model, last
         epoch trained, using the base class method
         save_last_model_to_file.
+    save_init_model : bool, default = False
+        Whether to save the initialization of the  model.
     best_file_name : str, default = "best_model"
         The name of the file of the best model, if
         save_best_model is set to False, this parameter
@@ -79,6 +81,10 @@ class AEBiGRUClusterer(BaseDeepClusterer):
         The name of the file of the last model, if
         save_last_model is set to False, this parameter
         is discarded.
+    init_file_name : str, default = "init_model"
+        The name of the file of the init model, if
+        save_init_model is set to False,
+        this parameter is discarded.
     callbacks : keras.callbacks, default = None
         List of keras callbacks.
 
@@ -119,8 +125,10 @@ def __init__(
         file_path="./",
         save_best_model=False,
         save_last_model=False,
+        save_init_model=False,
         best_file_name="best_model",
-        last_file_name="last_file",
+        last_file_name="last_model",
+        init_file_name="init_model",
         callbacks=None,
     ):
         self.latent_space_dim = latent_space_dim
@@ -138,7 +146,9 @@ def __init__(
         self.n_epochs = n_epochs
         self.save_best_model = save_best_model
         self.save_last_model = save_last_model
+        self.save_init_model = save_init_model
         self.best_file_name = best_file_name
+        self.init_file_name = init_file_name
         self.random_state = random_state
 
         super().__init__(
@@ -230,6 +240,9 @@ def _fit(self, X):
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape)
 
+        if self.save_init_model:
+            self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
         if self.verbose:
             self.training_model_.summary()
 
@@ -280,6 +293,9 @@ def _fit(self, X):
 
         self._fit_clustering(X=X)
 
+        if self.save_last_model:
+            self.save_last_model_to_file(file_path=self.file_path)
+
         gc.collect()
 
         return self

aeon/clustering/deep_learning/_ae_dcnn.py

@@ -86,6 +86,8 @@ class AEDCNNClusterer(BaseDeepClusterer):
         Whether or not to save the last model, last
         epoch trained, using the base class method
         save_last_model_to_file.
+    save_init_model : bool, default = False
+        Whether to save the initialization of the  model.
     best_file_name : str, default = "best_model"
         The name of the file of the best model, if
         save_best_model is set to False, this parameter
@@ -94,6 +96,10 @@ class AEDCNNClusterer(BaseDeepClusterer):
         The name of the file of the last model, if
         save_last_model is set to False, this parameter
         is discarded.
+    init_file_name : str, default = "init_model"
+        The name of the file of the init model, if
+        save_init_model is set to False,
+        this parameter is discarded.
     callbacks : keras.callbacks, default = None
         List of keras callbacks.
 
@@ -137,8 +143,10 @@ def __init__(
         file_path="./",
         save_best_model=False,
         save_last_model=False,
+        save_init_model=False,
         best_file_name="best_model",
-        last_file_name="last_file",
+        last_file_name="last_model",
+        init_file_name="init_model",
         callbacks=None,
     ):
         self.latent_space_dim = latent_space_dim
@@ -160,7 +168,9 @@ def __init__(
         self.n_epochs = n_epochs
         self.save_best_model = save_best_model
         self.save_last_model = save_last_model
+        self.save_init_model = save_init_model
         self.best_file_name = best_file_name
+        self.init_file_name = init_file_name
         self.random_state = random_state
 
         super().__init__(
@@ -256,6 +266,9 @@ def _fit(self, X):
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape)
 
+        if self.save_init_model:
+            self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
         if self.verbose:
             self.training_model_.summary()
 
@@ -306,6 +319,9 @@ def _fit(self, X):
 
         self._fit_clustering(X=X)
 
+        if self.save_last_model:
+            self.save_last_model_to_file(file_path=self.file_path)
+
         gc.collect()
 
         return self

aeon/clustering/deep_learning/_ae_drnn.py

@@ -87,6 +87,8 @@ class AEDRNNClusterer(BaseDeepClusterer):
         Whether or not to save the last model, last
         epoch trained, using the base class method
         save_last_model_to_file.
+    save_init_model : bool, default = False
+        Whether to save the initialization of the  model.
     best_file_name : str, default = "best_model"
         The name of the file of the best model, if
         save_best_model is set to False, this parameter
@@ -95,6 +97,10 @@ class AEDRNNClusterer(BaseDeepClusterer):
         The name of the file of the last model, if
         save_last_model is set to False, this parameter
         is discarded.
+    init_file_name : str, default = "init_model"
+        The name of the file of the init model, if
+        save_init_model is set to False,
+        this parameter is discarded.
     callbacks : keras.callbacks, default = None
         List of keras callbacks.
 
@@ -139,8 +145,10 @@ def __init__(
         file_path="./",
         save_best_model=False,
         save_last_model=False,
+        save_init_model=False,
         best_file_name="best_model",
-        last_file_name="last_file",
+        last_file_name="last_model",
+        init_file_name="init_model",
         callbacks=None,
     ):
         self.latent_space_dim = latent_space_dim
@@ -163,7 +171,9 @@ def __init__(
         self.n_epochs = n_epochs
         self.save_best_model = save_best_model
         self.save_last_model = save_last_model
+        self.save_init_model = save_init_model
         self.best_file_name = best_file_name
+        self.init_file_name = init_file_name
         self.random_state = random_state
 
         super().__init__(
@@ -260,6 +270,9 @@ def _fit(self, X):
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape)
 
+        if self.save_init_model:
+            self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
         if self.verbose:
             self.training_model_.summary()
 
@@ -312,6 +325,9 @@ def _fit(self, X):
 
         self._fit_clustering(X=X)
 
+        if self.save_last_model:
+            self.save_last_model_to_file(file_path=self.file_path)
+
         gc.collect()
 
         return self

aeon/clustering/deep_learning/_ae_fcn.py

@@ -88,6 +88,8 @@ class AEFCNClusterer(BaseDeepClusterer):
         Whether or not to save the last model, last
         epoch trained, using the base class method
         save_last_model_to_file.
+    save_init_model : bool, default = False
+        Whether to save the initialization of the  model.
     best_file_name : str, default = "best_model"
         The name of the file of the best model, if
         save_best_model is set to False, this parameter
@@ -96,6 +98,10 @@ class AEFCNClusterer(BaseDeepClusterer):
         The name of the file of the last model, if
         save_last_model is set to False, this parameter
         is discarded.
+    init_file_name : str, default = "init_model"
+        The name of the file of the init model, if
+        save_init_model is set to False,
+        this parameter is discarded.
     callbacks : keras.callbacks, default = None
         List of keras callbacks.
 
@@ -147,8 +153,10 @@ def __init__(
         file_path="./",
         save_best_model=False,
         save_last_model=False,
+        save_init_model=False,
         best_file_name="best_model",
-        last_file_name="last_file",
+        last_file_name="last_model",
+        init_file_name="init_model",
         callbacks=None,
     ):
         self.latent_space_dim = latent_space_dim
@@ -171,7 +179,9 @@ def __init__(
         self.n_epochs = n_epochs
         self.save_best_model = save_best_model
         self.save_last_model = save_last_model
+        self.save_init_model = save_init_model
         self.best_file_name = best_file_name
+        self.init_file_name = init_file_name
         self.random_state = random_state
 
         super().__init__(
@@ -271,6 +281,9 @@ def _fit(self, X):
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape)
 
+        if self.save_init_model:
+            self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
         if self.verbose:
             self.training_model_.summary()
 
@@ -332,6 +345,9 @@ def _fit(self, X):
 
         self._fit_clustering(X=X)
 
+        if self.save_last_model:
+            self.save_last_model_to_file(file_path=self.file_path)
+
         gc.collect()
 
         return self

aeon/clustering/deep_learning/_ae_resnet.py

@@ -91,12 +91,18 @@ class AEResNetClusterer(BaseDeepClusterer):
     save_last_model : bool, default = False
         Whether or not to save the last model, last epoch trained, using the base
         class method save_last_model_to_file.
+    save_init_model : bool, default = False
+        Whether to save the initialization of the  model.
     best_file_name : str, default = "best_model"
         The name of the file of the best model, if save_best_model is set to
         False, this parameter is discarded.
     last_file_name : str, default = "last_model"
         The name of the file of the last model, if save_last_model is set to
         False, this parameter is discarded.
+    init_file_name : str, default = "init_model"
+        The name of the file of the init model, if
+        save_init_model is set to False,
+        this parameter is discarded.
     verbose : boolean, default = False
         whether to output extra information
     loss : string, default = "mean_squared_error"
@@ -155,8 +161,10 @@ def __init__(
         file_path="./",
         save_best_model=False,
         save_last_model=False,
+        save_init_model=False,
         best_file_name="best_model",
-        last_file_name="last_file",
+        last_file_name="last_model",
+        init_file_name="init_model",
         optimizer="Adam",
     ):
         self.n_residual_blocks = n_residual_blocks
@@ -179,8 +187,9 @@ def __init__(
         self.file_path = file_path
         self.save_best_model = save_best_model
         self.save_last_model = save_last_model
+        self.save_init_model = save_init_model
         self.best_file_name = best_file_name
-        self.last_file_name = last_file_name
+        self.init_file_name = init_file_name
         self.optimizer = optimizer
 
         self.history = None
@@ -284,6 +293,9 @@ def _fit(self, X):
         self.input_shape = X.shape[1:]
         self.training_model_ = self.build_model(self.input_shape)
 
+        if self.save_init_model:
+            self.training_model_.save(self.file_path + self.init_file_name + ".keras")
+
         if self.verbose:
             self.training_model_.summary()
 
@@ -347,6 +359,9 @@ def _fit(self, X):
 
         self._fit_clustering(X=X)
 
+        if self.save_last_model:
+            self.save_last_model_to_file(file_path=self.file_path)
+
         gc.collect()
         return self