Using TF-IDF Vectors and Sparse Matrices: A Deep Dive into the TfidfVectorizer

In this article, we will explore how to iterate over each document in a text corpus and run it through the TfidfVectorizer while storing the output in a sparse matrix. This is a fundamental concept in natural language processing (NLP) that enables us to efficiently represent text data as numerical vectors.

Introduction to TF-IDF

TF-IDF, or Term Frequency-Inverse Document Frequency, is a technique used to weight the importance of words in a document based on their frequency and rarity across the entire corpus. This allows us to capture both local and global patterns in the text data. The TfidfVectorizer in scikit-learn provides an efficient way to perform TF-IDF calculations.

Understanding Sparse Matrices

A sparse matrix is a matrix where most of the elements are zero, representing missing or irrelevant information. In the context of TF-IDF, the sparse matrix represents the word-document co-occurrence patterns in the corpus. The tocoo() method returns the transpose of the sparse matrix, which contains the indices of non-zero values.

Iterating Over Documents with TfidfVectorizer

Let’s dive into the code snippet provided by the OP:

for i in range(test_reviews_df.shape[0]):
    final_tf_idf=[]
    final_tf_idf.append(text_vectorizer_tst.transform(
        [test_reviews_df['formated_reviews'].values[i]]))
    final_tf_idf=final_tf_idf.tocoo()

This code attempts to iterate over each document in the test_reviews_df DataFrame and apply the TfidfVectorizer on it. However, this approach is incorrect because transform() returns a sparse matrix, not a list of vectors.

Correct Approach

To fix this issue, we need to reshape the output of transform() to obtain a list of vectors, each representing a document in the corpus. We can achieve this using the following code:

for i in range(test_reviews_df.shape[0]):
    doc_vector = text_vectorizer_tst.transform(
        [test_reviews_df['formated_reviews'].values[i]])
    final_tf_idf.append(doc_vector.tocoo())

In this corrected version, we append each sparse matrix obtained by transform() to the final_tf_idf list. This allows us to store the output of TF-IDF calculations for each document in the corpus.

Extracting Co-occurrence Patterns

The tocoo() method returns a tuple containing three values:

row : The row index of the non-zero elements in the sparse matrix.
col : The column index of the non-zero elements in the sparse matrix.
data : A list of non-zero element values.

We can extract these values to obtain the co-occurrence patterns between words and documents:

for i, (row, col, data) in enumerate(final_tf_idf):
    print(f"Document {i+1}:")
    for j, value in zip(row, col):
        print(f"{test_reviews_df['formated_reviews'].values[i][j]}: {value}")

This code prints the co-occurrence patterns between words and documents for each document in the corpus.

Conclusion

In this article, we discussed how to iterate over each document in a text corpus using TfidfVectorizer while storing the output in a sparse matrix. We corrected an incorrect approach by reshaping the output of transform() and demonstrated how to extract co-occurrence patterns from the resulting sparse matrices. Understanding TF-IDF vectors and sparse matrices is essential for efficient NLP applications, such as text classification, clustering, and recommendation systems.

Code

Here’s a complete code example demonstrating the correct approach:

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd

# Create a sample DataFrame
test_reviews_df = pd.DataFrame({
    'formated_reviews': [
        "The quick brown fox jumps over the lazy dog",
        "The sun is shining brightly in the clear blue sky",
        "The cat purrs contentedly on my lap"
    ]
})

# Initialize TfidfVectorizer
text_vectorizer_tst = TfidfVectorizer()

# Fit the vectorizer to the data and transform it into a matrix of TF-IDF features
tf_idf_matrix = text_vectorizer_tst.fit_transform(test_reviews_df['formated_reviews'])

# Get the shape of the resulting matrix
print("TF-IDF Matrix Shape:", tf_idf_matrix.shape)

# Iterate over each document in the corpus and apply TF-IDF calculations
for i in range(tf_idf_matrix.shape[0]):
    doc_vector = tf_idf_matrix[i:i+1].tocoo()
    
    # Print co-occurrence patterns for this document
    print("Document", i+1)
    for j, (row, col, data) in enumerate(doc_vector):
        print(f"Word {test_reviews_df['formated_reviews'].values[j]}: Co-occurrences with Document {i+1}")
        print(data)

# Print the vocabulary of the vectorizer
print("TF-IDF Vocabulary:")
for vocab_word, _ in text_vectorizer_tst.vocabulary_.items():
    print(vocab_word)

In this code example, we create a sample DataFrame containing three documents and initialize TfidfVectorizer. We fit the vectorizer to the data and transform it into a matrix of TF-IDF features. Then, we iterate over each document in the corpus and apply TF-IDF calculations using the tocoo() method. Finally, we print co-occurrence patterns for each document and the vocabulary of the vectorizer.

Last modified on 2025-03-30