TensorFlow `edit_distance`: Calculating Levenshtein Distance in TensorFlow

In the world of natural language processing (NLP) and text analytics, the Levenshtein distance is a crucial metric for quantifying how dissimilar two strings are. It represents the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. When working with a library like TensorFlow, which excels in handling complex operations at scale, you have the ability to efficiently calculate this distance using its powerful features.

TensorFlow provides the edit_distance function, which can compute the Levenshtein distance between a hypothesis sequence and a truth sequence. In this article, we’ll walk through how to use TensorFlow's edit_distance to determine the similarity between sequences with practical examples and code snippets.

Understanding TensorFlow's edit_distance

The edit_distance operation in TensorFlow requires two main inputs: the hypothesis and the truth, both of which are expected to be sequences. Typically, these sequences are represented using sparse tensors which help optimize storage and operation efficiency, especially for natural language data that often includes varying lengths.

The function signature in TensorFlow looks like this:

tf.edit_distance(hypothesis, truth, normalize=True)

Here's a brief breakdown of the parameters:

• hypothesis: A SparseTensor representing the hypothesized or predicted sequence.
• truth: A SparseTensor representing the true or correct sequence.
• normalize: A boolean value. If set to True, the edit distance will be normalized by dividing the number of edits by the length of the truth.

Creating Sparse Tensors

Let's first understand how to create sparse tensors, which is essential before using edit_distance. Sparse tensors help manage varying lengths of sequences and memory by only storing non-zero values.

import tensorflow as tf

# Define sequences as lists of strings
hypothesis_sequence = ['Tensor', 'Flow']
truth_sequence = ['Tensor', 'Flows']

# Convert sequences to sparse tensors
hypothesis_sparse = tf.sparse.SparseTensor(indices=[[0, 0], [1, 0]],
                                           values=hypothesis_sequence,
                                           dense_shape=[len(hypothesis_sequence), 1])

truth_sparse = tf.sparse.SparseTensor(indices=[[0, 0], [1, 0]],
                                       values=truth_sequence,
                                       dense_shape=[len(truth_sequence), 1])

Calculating the Edit Distance

Now that we have our sequences as sparse tensors, we can calculate the edit distance between them:

# Calculate the edit distance
edit_distance = tf.edit_distance(hypothesis_sparse, truth_sparse, normalize=False)

# Evaluate the tensor using a session in TensorFlow 1.x or directly in TensorFlow 2.x
# TensorFlow 1.x style
with tf.Session() as sess:
    result = sess.run(edit_distance)
    print("Edit Distance:", result)

# TensorFlow 2.x style
result = edit_distance.numpy()
print("Edit Distance:", result)

The result will give you the number of edits required to transform the hypothesis into the truth. When using TensorFlow 2.x, you can directly use edit_distance.numpy() to evaluate the tensor, thanks to eager execution.

Using Normalized Edit Distance

Normalization can be helpful when comparing sequences of different lengths, as it provides a distance that's independent of sequence length:

# Calculate the normalized edit distance
normalized_edit_distance = tf.edit_distance(hypothesis_sparse, truth_sparse, normalize=True)

# TensorFlow 2.x style
normalized_result = normalized_edit_distance.numpy()
print("Normalized Edit Distance:", normalized_result)

The normalized edit distance yields a value between 0 and 1, where 0 indicates identical sequences, and a value closer to 1 means very different sequences.

Conclusion

Leveraging the capabilities of TensorFlow's edit_distance function enables developers to efficiently calculate the Levenshtein distance for various NLP tasks. It handles sequences robustly using sparse tensors, accommodating various lengths and complexities typical in real-world data. Whether you're working on auto-correction systems, plagiarism detection, or simply need to compare string similarities, edit_distance is a powerful function to include in your text-processing toolbox.