5 Best Ways to Find Out If Two Expression Trees are Equivalent Using Python

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πŸ’‘ Problem Formulation: Determining the equivalence of two expression trees in Python is crucial for optimizing compilers, constructing test cases, or simplifying algebraic expressions. An expression tree is a binary tree where leaves represent operands and interior nodes represent operators. The goal is to check if two such trees yield the same output regardless of their structure. For instance, trees for expressions (a+b)+c and a+(b+c) should be recognized as equivalent.

Method 1: Recursive Tree Comparison

The recursive tree comparison method involves traversing each tree simultaneously and comparing corresponding nodes for equivalence. This is predicated on the trees having identical topology and node values.

Here’s an example:

def check_equivalence(node1, node2):
    if node1 is None and node2 is None:
        return True
    if node1 is None or node2 is None:
        return False
    return (node1.value == node2.value and 
            check_equivalence(node1.left, node2.left) and 
            check_equivalence(node1.right, node2.right))

# Example usage
# Assuming 'tree1' and 'tree2' are already defined expression trees
print(check_equivalence(tree1, tree2))

Output: True or False

This method is straightforward; it checks whether both trees are equal by traversing from the root to the leaves. If all the corresponding nodes are equal, it returns True, otherwise False.

Method 2: Canonical Form Comparison

By converting both trees to a canonical form and then comparing, the Canonical Form Comparison method ensures that different tree structures representing the same expression are recognized as equivalent.

Here’s an example:

def tree_to_canonical(node):
    # This function would convert an expression tree 
    # to its canonical form

canonical_tree1 = tree_to_canonical(tree1)
canonical_tree2 = tree_to_canonical(tree2)

print(canonical_tree1 == canonical_tree2)

Output: True or False

By converting the trees into a predefined canonical form, we ensure that structural differences which do not affect the outcome of the expression do not lead to false negatives when checking for equivalence.

Method 3: Hashing (Fingerprinting) Trees

Hashing trees involves creating a unique hash for each tree based on its structure and values, with equivalent trees yielding the same hash value.

Here’s an example:

def hash_tree(node):
    # Compute a hash of the node's value combined with its children's hashes

hash1 = hash_tree(tree1)
hash2 = hash_tree(tree2)

print(hash1 == hash2)

Output: True or False

This method quickly compares trees without needing to traverse them entirelyβ€”it’s efficient if hashing is done correctly, ensuring that different trees do not end up with the same hash unintentionally.

Method 4: Symbolic Manipulation and Simplification

Symbolic manipulation involves simplifying the expression represented by each tree and checking if the simplified expressions are equivalent.

Here’s an example:

import sympy

def simplify_tree(tree):
    # Simplify the expression represented by the tree using sympy

simplified_tree1 = simplify_tree(tree1)
simplified_tree2 = simplify_tree(tree2)

print(sympy.simplify(simplified_tree1 - simplified_tree2) == 0)

Output: True or False

This method uses the power of symbolic computation to check for equivalence, beneficial for complex algebraic trees.

Bonus One-Liner Method 5: In-Order Traversal Comparison

By performing an in-order traversal of each tree, we can compare the sequence of nodes visited for equivalence.

Here’s an example:

def in_order_traversal(node):
    # Return a list of nodes in in-order traversal

print(in_order_traversal(tree1) == in_order_traversal(tree2))

Output: True or False

This method can be a quick check if both trees have similar structure and order, but vary in their internal arrangement of nodes.


  • Method 1: Recursive Tree Comparison. Simple to implement and understand. Performs well for identical trees, but cannot recognize equivalent expressions with different structures.
  • Method 2: Canonical Form Comparison. Robust in identifying structurally different but equivalent trees. Relies on an adequate definition of canonical form and can be complex to implement.
  • Method 3: Hashing Trees. Very fast and does not require tree traversal for comparison. Risk of hash collisions, which requires careful hash function design.
  • Method 4: Symbolic Manipulation. Powerful for complex trees and recognizes a broad range of equivalences. However, it relies on external libraries and can be computationally expensive.
  • Bonus Method 5: In-Order Traversal Comparison. Fast and easy to code. Only effective for trees that are identical or very similar in structure.