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@@ -79,6 +79,7 @@ class ILSPopSurrogate(Algorithm):
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validator, maximise, parent)
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self._n_local_search = 0
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+ self._ls_local_search = 0
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self._main_evaluator = evaluator
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self._surrogate_file_path = surrogate_file_path
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@@ -321,13 +322,13 @@ class ILSPopSurrogate(Algorithm):
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training_surrogate_every = int(r_squared * self._ls_train_surrogate)
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print(f"=> R² of surrogate is of {r_squared}.")
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print(f"=> MAE of surrogate is of {mae}.")
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- print(f'=> Retraining model every {training_surrogate_every} LS ({self._n_local_search % training_surrogate_every} of {training_surrogate_every})')
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+ print(f'=> Retraining model every {training_surrogate_every} LS ({self._ls_local_search % training_surrogate_every} of {training_surrogate_every})')
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# avoid issue when lauching every each local search
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if training_surrogate_every <= 0:
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training_surrogate_every = 1
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# check if necessary or not to train again surrogate
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- if self._n_local_search % training_surrogate_every == 0 and self._start_train_surrogate <= self.getGlobalEvaluation():
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+ if self._ls_local_search % training_surrogate_every == 0 and self._start_train_surrogate <= self.getGlobalEvaluation():
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# train again surrogate on real evaluated solutions file
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start_training = time.time()
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@@ -339,8 +340,12 @@ class ILSPopSurrogate(Algorithm):
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# reload new surrogate function
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self.load_surrogate()
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+ # reinit ls search
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+ self._ls_local_search = 0
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+
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# increase number of local search done
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self._n_local_search += 1
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+ self._ls_local_search += 1
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self.information()
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