Code Duplication    Length = 22-22 lines in 2 locations

src_old/hyperactive/optimizers/hyper_optimizer.py 1 location

@@ 56-77 (lines=22) @@
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        else:
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            self.verbosity = []
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    def convert_results2hyper(self):
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        self.eval_times = sum(self.gfo_optimizer.eval_times)
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        self.iter_times = sum(self.gfo_optimizer.iter_times)
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        if self.gfo_optimizer.best_para is not None:
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            value = self.hg_conv.para2value(self.gfo_optimizer.best_para)
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            position = self.hg_conv.position2value(value)
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            best_para = self.hg_conv.value2para(position)
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            self.best_para = best_para
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        else:
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            self.best_para = None
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        self.best_score = self.gfo_optimizer.best_score
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        self.positions = self.gfo_optimizer.search_data
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        self.search_data = self.hg_conv.positions2results(self.positions)
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        results_dd = self.gfo_optimizer.search_data.drop_duplicates(
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            subset=self.s_space.dim_keys, keep="first"
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        )
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        self.memory_values_df = results_dd[
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            self.s_space.dim_keys + ["score"]
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        ].reset_index(drop=True)
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    def _setup_process(self, nth_process):
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        self.nth_process = nth_process

src/hyperactive/optimizers/search.py 1 location

@@ 58-79 (lines=22) @@
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        self.max_time = max_time
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        self.nth_process = nth_process
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    def convert_results2hyper(self):
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        self.eval_times = sum(self.gfo_optimizer.eval_times)
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        self.iter_times = sum(self.gfo_optimizer.iter_times)
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        if self.gfo_optimizer.best_para is not None:
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            value = self.hg_conv.para2value(self.gfo_optimizer.best_para)
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            position = self.hg_conv.position2value(value)
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            best_para = self.hg_conv.value2para(position)
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            self.best_para = best_para
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        else:
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            self.best_para = None
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        self.best_score = self.gfo_optimizer.best_score
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        self.positions = self.gfo_optimizer.search_data
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        self.search_data = self.hg_conv.positions2results(self.positions)
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        results_dd = self.gfo_optimizer.search_data.drop_duplicates(
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            subset=self.s_space.dim_keys, keep="first"
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        )
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        self.memory_values_df = results_dd[
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            self.s_space.dim_keys + ["score"]
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        ].reset_index(drop=True)
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    def _setup_process(self):
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        self.hg_conv = HyperGradientConv(self.s_space)