Consistency of the predictions

[feroi35]

Hello,

When predicting the label of a sample with prediction_value = 0.0, the predicted class is 1 with the "greater or equal" inequality in groot/toolbox.py:

def predict(self, X):
        prediction_values = self.decision_function(X)
        if self.n_classes == 2:
            return (prediction_values >= 0).astype(int)
        else:
            return np.argmax(prediction_values, axis=1)

So it is for adversarial accuracy with tree ensembles, as the inequality is also "greater or equal" in groot.verification/kantchelian_attack.py:

def optimal_adversarial_example(self, sample, label):
        if self.binary:
            pred = (
                1 if self.check(sample, self.json_model) >= self.pred_threshold else 0
            )

with self.pred_threshold = 0.0

However, when computing the adversarial accuracy for a single decision tree, in groot/verification/decision_tree_attack.py, the inequality is strict.

def adversarial_examples(self, X, y, order, options={}):
        # Turn 'leaves' into bounding boxes and leaf prediction values
        bound_dicts, leaf_values = zip(*self.leaves)
        predictions = [value > 0 for value in leaf_values]

For consistency, I think the inequality should be large too