Retrieve Decision Boundary Lines (x,y coordinate format) from SKlearn Decision Tree

Question

I am trying to create a surface plot on an external visualization platform. I\'m working with the iris data set that is featured on the sklearn decision tree documentation p

Answer 1

For those interested, I had to recently also implement this for higher dimensional data, code was as follow:

number_of_leaves = (tree.tree_.children_left == -1).sum()
features = x.shape[1]
boundaries = np.zeros([number_of_leaves, features, 2])
boundaries[:,:,0] = -np.inf
boundaries[:,:,1] = np.inf

locs = np.where(tree.tree_.children_left == -1)[0]

for k in range(locs.shape[0]):
    idx = locs[k]
    idx_new = idx

    while idx_new != 0:
        i_check = np.where(tree.tree_.children_left == idx_new)[0]
        j_check = np.where(tree.tree_.children_right == idx_new)[0]

        if i_check.shape[0] == 1:
            idx_new = i_check[0]
            feat_ = tree.tree_.feature[idx_new]
            val_ = tree.tree_.value[idx_new]
            boundaries[k,feat_, 0] = val_
        elif j_check.shape[0] == 1:
            idx_new = j_check[0]
            feat_ = tree.tree_.feature[idx_new]
            val_ = tree.tree_.value[idx_new]
            boundaries[k,feat_, 1] = val_ 
        else: 
            print('Fail Case') # for debugging only - never occurs

Essentially I build up a n*d*2 tensor where n is the number of leaves of the tree, d is the dimensionality of the space and the third dimension holds the min and max values. Leaves are stored in tree.tree_.children_left / tree.tree_.children_right as -1, I then loop backwards to find the branch that caused the split onto the leaf and add the splitting criteria to the decision bounds.