How to get the coef_ and intercept_ according to the threshold about linear-tree HOT 2 CLOSED

Hi, I have difficulties understanding what you are trying to achieve... However, I can say to you that using the summary functionality you can get:

The keys are the integer map of each node. The values are dicts containing information for that node:

• 'col' (^): column used for splitting;
• 'th' (^): threshold value used for splitting in the selected column;
• 'loss': loss computed at node level. Weighted sum of children' losses if it is a splitting node;
• 'samples': number of samples in the node. Sum of children' samples if it is a split node;
• 'children' (^): integer mapping of possible children nodes;
• 'models': fitted linear models built in each split. Single model if it is leaf node;
• 'classes' (^^): target classes detected in the split. Available only for LinearTreeClassifier.
• (^): Only for split nodes. (^^): Only for leaf nodes.

If you want to get coef_ and intercept_, simply querying the interested 'models' inside the summary does the job.
As an additional resource, I can suggest this post where coef_ are used

from linear-tree.

Thanks.
Eventually I want to generate a formula below semi-automatically.
y = 3 * x + 0.3 if x <= 0,
y = 2 * x - 0.5 if 0.2 < x <= 0.5,
y = 0.5*x +0.1 if 0.5 =x

When there is only one explanatory variable, these formulas are explainable and can be used as white-box AI.

To create this formula, I need a threshold, a coefficient, and an intercept, so I created the following code. However, this code can't handle complex trees, so I wanted to ask if the library you created already has such a feature.

def func(x, result):
    threshold = result.get("threshold", None)
    coef_ = result.get("coef_", None)
    intercept_ = result.get("intercept_", None)

    if threshold is None:
        coef_ = coef_
        intercept_ = intercept_
    else:
        if x <= threshold:
            coef_ = coef_[0]
            intercept_ = intercept_[0]
        elif x > threshold:
            coef_ = coef_[1]
            intercept_ = intercept_[1]
        else:
            raise ValueError    
    y = x * coef_ + intercept_    
    return y

from linear-tree.