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@@ -298,15 +298,16 @@ class ILSPopSurrogate(Algorithm):
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self.increaseEvaluation()
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+ print(f'=================================================================')
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print(f'Best solution found so far: {self.result.fitness}')
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# check using specific dynamic criteria based on r^2
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r_squared = self._surrogate.analysis.coefficient_of_determination(self._surrogate.surrogate)
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mae = self._surrogate.analysis.mae(self._surrogate.surrogate)
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training_surrogate_every = int(r_squared * self._ls_train_surrogate)
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- print(f"=> R^2 of surrogate is of {r_squared}. Retraining model every {training_surrogate_every} LS")
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- print(f"=> MAE of surrogate is of {mae}. Retraining model every {training_surrogate_every} LS")
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-
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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} 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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