Optimizing TF-IDF Similarity DataFrames in Python

Introduction

TF-IDF (Term Frequency-Inverse Document Frequency) is a widely used technique for text preprocessing and feature extraction. It calculates the importance of each word in a document based on its frequency and rarity across a corpus. The resulting matrix, where each row represents a document and each column represents a word, can be used as input to machine learning algorithms for tasks like text classification, clustering, and topic modeling.

However, when working with large TF-IDF matrices, common issues arise, such as performance degradation and memory consumption. In this article, we’ll explore how to optimize the creation of TF-IDF similarity dataframes using Python’s scikit-learn library and the hashingvectorizer.

Background

Before diving into the solution, let’s quickly review the basics of TF-IDF:

• Term Frequency (TF): calculates the frequency of each word in a document.
• Inverse Document Frequency (IDF): measures the rarity of each word across a corpus.
• Weighted Sum: combines TF and IDF to calculate the weighted sum for each word in a document.

Vectorization Techniques

In scikit-learn, vectorization refers to the process of converting text data into numerical representations. There are two primary types:

• Sparse Vectorization: uses TfidfVectorizer (or its variants) for efficient storage and computation.
• Dense Vectorization: uses CountVectorizer or HashingVectorizer (more on this later).

Densely Vectors: CountVectorizer

The CountVectorizer creates a sparse matrix where each row represents a document, with values being the frequency of each word in that document. However, when you want to compute similarities between all documents, you’re essentially performing a dot product operation, which can be computationally expensive.

Hashing Vectorizer: A More Efficient Alternative

The HashingVectorizer is a variant of the CountVectorizer that uses hashing instead of sparse matrices. This approach allows for more efficient similarity computations and can handle large datasets without significant performance degradation.

When using the HashingVectorizer, you’ll need to specify the n_features parameter, which determines the number of unique features (words) in your corpus. This value should be equal to the number of rows in your TF-IDF matrix.

Optimizing Similarity Computation

To optimize similarity computation when working with large TF-IDF matrices, we can use the following techniques:

• Parallelize using multi-threading: scikit-learn provides n_jobs parameter for parallelizing computations. By setting this parameter, you can leverage multiple CPU cores to speed up your computations.
• Distributed computing: if you have a large-scale dataset that doesn’t fit in memory, consider distributing your computation across multiple machines using tools like Dask or joblib.

Code Example: Optimizing TF-IDF Similarity Computation

import numpy as np
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
import time


# Sample dataset ( replace with your own data )
df = pd.DataFrame({
    'text': ['This is a sample text.', 'Another example for demonstration.', 'Sample text with multiple words.'],
    'index_name': [0, 1, 2]
})

n_features = len(df['text'].unique())
vectorizer = HashingVectorizer(n_features=n_features, n_jobs=-1)

# Fit transform the data
tfidf_matrix = vectorizer.fit_transform(df['text'])

# Compute cosine similarity between all pairs of documents
start_time = time.time()
cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)
end_time = time.time()

print(f"Time taken: {end_time - start_time} seconds")


similarity_df = pd.DataFrame(columns=['a', 'b', 'similarity'])
for n in range(len(cosine_sim)):
    for i in list(enumerate(cosine_sim[n])):
        if i[1] > 0.8 and i[1] < 0.99:
            similarity_df = similarity_df._append({'column_0': df['index_name'].iloc[n], 'column_1': df['index_name'].iloc[i[0]], 'similarity': i[1]}, ignore_index=True)
print(similarity_df.head())

Key Takeaways

• Use the HashingVectorizer for efficient similarity computations.
• Optimize your computation by parallelizing using multi-threading or distributed computing when working with large datasets.

By applying these techniques, you can significantly improve the performance and efficiency of your TF-IDF similarity computations.

Last modified on 2025-02-25