def compute_probabilities()

in per_class_augmentation/augmentations.py [0:0]

• 15 lines of code
• 5 McCabe index (conditional complexity)

    def compute_probabilities(self, df: pd.DataFrame):
        num_transforms = len(df)
        if self.transform_prob["dist"] == "fixed":
            val = self.transform_prob["fixed_prob"]
            probabilities = uniform_dist(val=val, size=num_transforms)
        elif self.transform_prob["dist"] == "uniform":
            probabilities = uniform_dist(size=num_transforms)
        elif self.transform_prob["dist"] == "weighted_boost":
            if len(df) == 1:
                probabilities = [0.5]
            else:
                probabilities = softmax(df["weighted_boost"].values)
        else:
            raise NotImplementedError(f"{self.transform_prob=} not supported")
        return probabilities