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sample_weight when training with full data
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@ -146,7 +146,9 @@ class AutoMLState:
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else:
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sampled_X_train, sampled_y_train = concat(self.X_train,
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self.X_val), np.concatenate([self.y_train, self.y_val])
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sampled_weight = self.fit_kwargs.get('sample_weight')
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weight = self.fit_kwargs.get('sample_weight')
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if weight is not None:
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sampled_weight = np.concatenate([weight, self.weight_val])
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return sampled_X_train, sampled_y_train, sampled_weight
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def _compute_with_config_base(self,
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