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https://github.com/ANL-CEEESA/MIPLearn.git
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Primal: Refactor stats
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@@ -63,31 +63,32 @@ class PrimalSolutionComponent(Component):
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self.threshold_prototype = threshold
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self.classifier_prototype = classifier
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self.stats: Dict[str, float] = {}
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self._n_free = 0
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self._n_zero = 0
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self._n_one = 0
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def before_solve_mip(self, solver, instance, model):
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if len(self.thresholds) > 0:
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logger.info("Predicting primal solution...")
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solution = self.predict(instance.features, instance.training_data[-1])
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logger.info("Predicting MIP solution...")
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solution = self.predict(
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instance.features,
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instance.training_data[-1],
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)
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# Collect prediction statistics
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self._n_free = 0
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self._n_zero = 0
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self._n_one = 0
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self.stats["Primal: Free"] = 0
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self.stats["Primal: Zero"] = 0
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self.stats["Primal: One"] = 0
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for (var, var_dict) in solution.items():
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for (idx, value) in var_dict.items():
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if value is None:
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self._n_free += 1
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self.stats["Primal: Free"] += 1
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else:
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if value < 0.5:
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self._n_zero += 1
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self.stats["Primal: Zero"] += 1
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else:
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self._n_one += 1
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self.stats["Primal: One"] += 1
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logger.info(
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f"Predicted: free: {self._n_free}, zero: {self._n_zero}, "
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f"one: {self._n_one}"
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f"Predicted: free: {self.stats['Primal: Free']}, "
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f"zero: {self.stats['Primal: zero']}, "
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f"one: {self.stats['Primal: One']}"
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)
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# Provide solution to the solver
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@@ -104,9 +105,7 @@ class PrimalSolutionComponent(Component):
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stats: LearningSolveStats,
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training_data: TrainingSample,
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) -> None:
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stats["Primal: free"] = self._n_free
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stats["Primal: zero"] = self._n_zero
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stats["Primal: one"] = self._n_one
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stats.update(self.stats)
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def fit_xy(
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self,
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