[Solved] What is the bug in this code that calculates cosine similarity? from sklearn.metrics.... What is the bug in this code that calculates cosine similarity? | NLP

NLP - Text Similarity and Search

What is the bug in this code that calculates cosine similarity? from sklearn.metrics.pairwise import cosine_similarity import numpy as np v1 = np.array([1, 2, 3]) v2 = np.array([4, 5, 6]) result = cosine_similarity(v1, v2) print(result)

Acosine_similarity requires lists, not numpy arrays

Bcosine_similarity only works for sparse matrices

CInput vectors must be 2D arrays, not 1D

DNo bug, code is correct

Step-by-Step Solution

Solution:

Step 1: Check input format for cosine_similarity
sklearn's cosine_similarity expects 2D arrays (shape: [n_samples, n_features]).
Step 2: Identify input shape issue
v1 and v2 are 1D arrays; they should be reshaped to 2D, e.g., v1.reshape(1, -1).
Final Answer:
Input vectors must be 2D arrays, not 1D -> Option C
Quick Check:
Input shape must be 2D for sklearn cosine_similarity [OK]

Quick Trick: Reshape vectors to 2D before using sklearn cosine_similarity [OK]

Common Mistakes:

MISTAKES

Passing 1D arrays directly
Assuming cosine_similarity accepts lists only
Thinking it only works with sparse matrices

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What is the bug in this code that calculates cosine similarity?

Step 1: Check input format for cosine_similarity

Step 2: Identify input shape issue

Final Answer:

Quick Check:

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