Answer a question

I use sklearn.tree.DecisionTreeClassifier to build a decision tree. With the optimal parameter settings, I get a tree that has unnecessary leaves (see example picture below - I do not need probabilities, so the leaf nodes marked with red are a unnecessary split)

Is there any third-party library for pruning these unnecessary nodes? Or a code snippet? I could write one, but I can't really imagine that I am the first person with this problem...

Code to replicate:

from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target
mdl = DecisionTreeClassifier(max_leaf_nodes=8)
mdl.fit(X,y)

PS: I have tried multiple keyword searches and am kind of surprised to find nothing - is there really no post-pruning in general in sklearn?

PPS: In response to the possible duplicate: While the suggested question might help me when coding the pruning algorithm myself, it answers a different question - I want to get rid of leaves that do not change the final decision, while the other question wants a minimum threshold for splitting nodes.

PPPS: The tree shown is an example to show my problem. I am aware of the fact that the parameter settings to create the tree are suboptimal. I am not asking about optimizing this specific tree, I need to do post-pruning to get rid of leaves that might be helpful if one needs class probabilities, but are not helpful if one is only interested in the most likely class.

Answers

Using ncfirth's link, I was able to modify the code there so that it fits to my problem:

from sklearn.tree._tree import TREE_LEAF

def is_leaf(inner_tree, index):
    # Check whether node is leaf node
    return (inner_tree.children_left[index] == TREE_LEAF and 
            inner_tree.children_right[index] == TREE_LEAF)

def prune_index(inner_tree, decisions, index=0):
    # Start pruning from the bottom - if we start from the top, we might miss
    # nodes that become leaves during pruning.
    # Do not use this directly - use prune_duplicate_leaves instead.
    if not is_leaf(inner_tree, inner_tree.children_left[index]):
        prune_index(inner_tree, decisions, inner_tree.children_left[index])
    if not is_leaf(inner_tree, inner_tree.children_right[index]):
        prune_index(inner_tree, decisions, inner_tree.children_right[index])

    # Prune children if both children are leaves now and make the same decision:     
    if (is_leaf(inner_tree, inner_tree.children_left[index]) and
        is_leaf(inner_tree, inner_tree.children_right[index]) and
        (decisions[index] == decisions[inner_tree.children_left[index]]) and 
        (decisions[index] == decisions[inner_tree.children_right[index]])):
        # turn node into a leaf by "unlinking" its children
        inner_tree.children_left[index] = TREE_LEAF
        inner_tree.children_right[index] = TREE_LEAF
        ##print("Pruned {}".format(index))

def prune_duplicate_leaves(mdl):
    # Remove leaves if both 
    decisions = mdl.tree_.value.argmax(axis=2).flatten().tolist() # Decision for each node
    prune_index(mdl.tree_, decisions)

Using this on a DecisionTreeClassifier clf:

prune_duplicate_leaves(clf)

Edit: Fixed a bug for more complex trees