Remove clear memory

csrank/choicefunction/cmpnet_choice.py

@@ -187,6 +187,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        super().clear_memory(**kwargs)

csrank/choicefunction/fate_choice.py

@@ -192,7 +192,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        self.logger.info("Clearing memory")
-        super().clear_memory(**kwargs)

csrank/choicefunction/feta_choice.py

@@ -364,7 +364,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        self.logger.info("Clearing memory")
-        super().clear_memory(**kwargs)

csrank/choicefunction/ranknet_choice.py

@@ -181,7 +181,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        self.logger.info("Clearing memory")
-        super().clear_memory(**kwargs)

csrank/core/cmpnet_core.py

@@ -1,7 +1,6 @@
 from itertools import permutations
 import logging
 
-from keras import backend as K
 from keras import Input
 from keras import Model
 from keras.layers import concatenate
@@ -10,7 +9,6 @@
 from keras.regularizers import l2
 import numpy as np
 from sklearn.utils import check_random_state
-import tensorflow as tf
 
 from csrank.constants import allowed_dense_kwargs
 from csrank.layers import NormalizedDense
@@ -36,7 +34,6 @@ def __init__(
         self.logger = logging.getLogger("CmpNet")
         self.batch_normalization = batch_normalization
         self.activation = activation
-        self.hash_file = None
 
         self.batch_size = batch_size
 
@@ -197,31 +194,3 @@ def _predict_scores_fixed(self, X, **kwargs):
         self.logger.info("Done predicting scores")
 
         return scores
-
-    def clear_memory(self, **kwargs):
-        """
-            Clear the memory, restores the currently fitted model back to prevent memory leaks.
-
-            Parameters
-            ----------
-            **kwargs :
-                Keyword arguments for the function
-        """
-        if self.hash_file is not None:
-            self.model.save_weights(self.hash_file)
-            K.clear_session()
-            sess = tf.Session()
-            K.set_session(sess)
-
-            self._initialize_optimizer()
-            self._initialize_regularizer()
-            self._construct_layers(
-                kernel_regularizer=self.kernel_regularizer_,
-                kernel_initializer=self.kernel_initializer,
-                activation=self.activation,
-                **self.kwargs,
-            )
-            self.model = self.construct_model()
-            self.model.load_weights(self.hash_file)
-        else:
-            self.logger.info("Cannot clear the memory")

csrank/core/fate_network.py

@@ -1,6 +1,5 @@
 import logging
 
-import keras.backend as K
 from keras.layers import Dense
 from keras.layers import Input
 from keras.layers.merge import concatenate
@@ -9,7 +8,6 @@
 from keras.regularizers import l2
 import numpy as np
 from sklearn.utils import check_random_state
-import tensorflow as tf
 
 from csrank.constants import allowed_dense_kwargs
 from csrank.layers import create_input_lambda
@@ -185,7 +183,6 @@ def __init__(self, n_hidden_set_layers=1, n_hidden_set_units=1, **kwargs):
             kernel_regularizer=self.kernel_regularizer_,
         )
         self.is_variadic = True
-        self.hash_file = None
 
     def _create_set_layers(self, **kwargs):
         """
@@ -662,38 +659,3 @@ def _predict_scores_fixed(self, X, **kwargs):
         predicted_scores = joint_model.predict(X)
         self.logger.info("Done predicting scores")
         return predicted_scores
-
-    def clear_memory(self, n_objects=5, **kwargs):
-        """
-            Clear the memory, restores the currently fitted model back to prevent memory leaks.
-
-            Parameters
-            ----------
-            n_objects : int
-                float (n_instances, n_objects, n_features)
-            **kwargs :
-                Keyword arguments for the function
-        """
-        if self.hash_file is not None:
-            self.model.save_weights(self.hash_file)
-            K.clear_session()
-            sess = tf.Session()
-            K.set_session(sess)
-            self._initialize_optimizer()
-            self._initialize_regularizer()
-            self._construct_layers(
-                activation=self.activation,
-                kernel_initializer=self.kernel_initializer,
-                kernel_regularizer=self.kernel_regularizer_,
-                **self.kwargs,
-            )
-            self._create_set_layers(
-                activation=self.activation,
-                kernel_initializer=self.kernel_initializer,
-                kernel_regularizer=self.kernel_regularizer_,
-                **self.kwargs,
-            )
-            self.model = self.construct_model(self.n_object_features_fit_, n_objects)
-            self.model.load_weights(self.hash_file)
-        else:
-            self.logger.info("Cannot clear the memory")

csrank/core/feta_network.py

@@ -13,7 +13,6 @@
 from keras.regularizers import l2
 import numpy as np
 from sklearn.utils import check_random_state
-import tensorflow as tf
 
 from csrank.constants import allowed_dense_kwargs
 from csrank.layers import NormalizedDense
@@ -51,7 +50,6 @@ def __init__(
         self.max_number_of_objects = max_number_of_objects
         self.num_subsample = num_subsample
         self.batch_size = batch_size
-        self.hash_file = None
         self.optimizer = optimizer
         self.add_zeroth_order_model = add_zeroth_order_model
         self.n_hidden = n_hidden
@@ -307,8 +305,6 @@ def fit(
             verbose=verbose,
             **kwd,
         )
-        if self.hash_file is not None:
-            self.model.save_weights(self.hash_file)
 
     def sub_sampling(self, X, Y):
         if self.n_objects_fit_ > self.max_number_of_objects:
@@ -338,32 +334,3 @@ def _predict_scores_fixed(self, X, **kwargs):
             scores = self.model.predict(X, **kwargs)
         self.logger.info("Done predicting scores")
         return scores
-
-    def clear_memory(self, **kwargs):
-        """
-            Clear the memory, restores the currently fitted model back to prevent memory leaks.
-
-            Parameters
-            ----------
-            **kwargs :
-                Keyword arguments for the function
-        """
-        if self.hash_file is not None:
-            self.model.save_weights(self.hash_file)
-            K.clear_session()
-            sess = tf.Session()
-            K.set_session(sess)
-
-            self._pairwise_model = None
-            self._zero_order_model = None
-            self._initialize_optimizer()
-            self._construct_layers(
-                kernel_regularizer=self.kernel_regularizer_,
-                kernel_initializer=self.kernel_initializer,
-                activation=self.activation,
-                **self.kwargs,
-            )
-            self.model = self.construct_model()
-            self.model.load_weights(self.hash_file)
-        else:
-            self.logger.info("Cannot clear the memory")

csrank/core/ranknet_core.py

@@ -1,6 +1,5 @@
 import logging
 
-from keras import backend as K
 from keras import Input
 from keras import Model
 from keras.layers import add
@@ -9,7 +8,6 @@
 from keras.optimizers import SGD
 from keras.regularizers import l2
 from sklearn.utils import check_random_state
-import tensorflow as tf
 
 from csrank.constants import allowed_dense_kwargs
 from csrank.layers import NormalizedDense
@@ -50,7 +48,6 @@ def __init__(
         self.batch_size = batch_size
         self._scoring_model = None
         self.model = None
-        self.hash_file = None
         self.random_state = random_state
 
     def _construct_layers(self, **kwargs):
@@ -200,31 +197,3 @@ def _predict_scores_fixed(self, X, **kwargs):
         scores = scores.reshape(n_instances, n_objects)
         self.logger.info("Done predicting scores")
         return scores
-
-    def clear_memory(self, **kwargs):
-        """
-            Clear the memory, restores the currently fitted model back to prevent memory leaks.
-
-            Parameters
-            ----------
-            **kwargs :
-                Keyword arguments for the function
-        """
-        if self.hash_file is not None:
-            self.model.save_weights(self.hash_file)
-            K.clear_session()
-            sess = tf.Session()
-            K.set_session(sess)
-
-            self._scoring_model = None
-            self._initialize_optimizer()
-            self._construct_layers(
-                kernel_regularizer=self.kernel_regularizer_,
-                kernel_initializer=self.kernel_initializer,
-                activation=self.activation,
-                **self.kwargs,
-            )
-            self.model = self.construct_model()
-            self.model.load_weights(self.hash_file)
-        else:
-            self.logger.info("Cannot clear the memory")

csrank/discretechoice/cmpnet_discrete_choice.py

@@ -117,6 +117,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        super().clear_memory(**kwargs)

csrank/discretechoice/fate_discrete_choice.py

@@ -145,7 +145,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        self.logger.info("Clearing memory")
-        super().clear_memory(**kwargs)

csrank/discretechoice/feta_discrete_choice.py

@@ -334,7 +334,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        self.logger.info("Clearing memory")
-        super().clear_memory(**kwargs)

csrank/discretechoice/ranknet_discrete_choice.py

@@ -113,7 +113,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        self.logger.info("Clearing memory")
-        super().clear_memory(**kwargs)

csrank/learner.py

@@ -95,12 +95,6 @@ def predict_scores(self, X, **kwargs):
             scores = dict()
             for ranking_size, x in X.items():
                 n_instances, n_objects, n_features = x.shape
-                if "clear_memory" in dir(self):
-                    self.clear_memory(
-                        n_instances=n_instances,
-                        n_objects=n_objects,
-                        n_features=n_features,
-                    )
                 scores[ranking_size] = self._predict_scores_fixed(x, **kwargs)
 
         else:

csrank/objectranking/cmp_net.py

@@ -165,6 +165,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        super().clear_memory(**kwargs)

csrank/objectranking/fate_object_ranker.py

@@ -110,7 +110,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        self.logger.info("Clearing memory")
-        super().clear_memory(**kwargs)

csrank/objectranking/feta_object_ranker.py

@@ -149,7 +149,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        self.logger.info("Clearing memory")
-        super().clear_memory(**kwargs)

csrank/objectranking/list_net.py

@@ -1,14 +1,12 @@
 import logging
 
-from keras import backend as K
 from keras import Input
 from keras.layers import concatenate
 from keras.layers import Dense
 from keras.models import Model
 from keras.optimizers import SGD
 from keras.regularizers import l2
 from sklearn.utils import check_random_state
-import tensorflow as tf
 
 from csrank.constants import allowed_dense_kwargs
 from csrank.layers import create_input_lambda
@@ -52,8 +50,6 @@ def __init__(
             ----------
             n_top : int
                 Size of the top-k-subrankings to consider for training
-            hash_file: str
-                File path of the model where the weights are stored to get the predictions after clearing the memory
             n_hidden : int
                 Number of hidden layers used in the scoring network
             n_units : int
@@ -107,7 +103,6 @@ def __init__(
 
         self.batch_size = batch_size
         self.random_state = random_state
-        self.hash_file = None
         self.model = None
         self._scoring_model = None
 
@@ -262,30 +257,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        """
-            Clear the memory, restores the currently fitted model back to prevent memory leaks.
-
-            Parameters
-            ----------
-            **kwargs :
-                Keyword arguments for the function
-        """
-        if self.hash_file is not None:
-            self.model.save_weights(self.hash_file)
-            K.clear_session()
-            sess = tf.Session()
-            K.set_session(sess)
-            self._scoring_model = None
-            self._initialize_optimizer()
-            self._construct_layers(
-                kernel_regularizer=self.kernel_regularizer_,
-                kernel_initializer=self.kernel_initializer,
-                activation=self.activation,
-                **self.kwargs,
-            )
-            self.model = self.construct_model()
-            self.model.load_weights(self.hash_file)
-        else:
-            self.logger.info("Cannot clear the memory")

csrank/objectranking/rank_net.py

@@ -156,6 +156,3 @@ def predict_for_scores(self, scores, **kwargs):
 
     def predict(self, X, **kwargs):
         return super().predict(X, **kwargs)
-
-    def clear_memory(self, **kwargs):
-        super().clear_memory(**kwargs)