5 Best Ways to Calculate the Dot Product of Two Sparse Vectors in Python

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πŸ’‘ Problem Formulation: In numerical computing, calculating the dot product of two sparse vectors efficiently is crucial for performance. Sparse vectors are those with a majority of zero elements, commonly found in data science and machine learning. Given two sparse vectors, each represented as a dictionary of their non-zero index-value pairs, the goal is to calculate the dot product (scalar product) efficiently, demonstrating through Python code. For instance, if the input vectors are {1: 3, 4: 5} and {1: 7, 4: 2}, the desired output is 31 as the dot product.

Method 1: Using Dictionary Intersection

This method entails iterating over the intersection of the keys from both dictionaries, multiplying the corresponding values, and accumulating the products to obtain the dot product. This is efficient as it considers only the non-zero elements.

Here’s an example:

def sparse_dot_product(vec1, vec2):
    return sum(vec1[k] * vec2[k] for k in vec1 if k in vec2)

# Example Vectors
vec1 = {1: 3, 4: 5}
vec2 = {1: 7, 4: 2}

# Calculate Dot Product
result = sparse_dot_product(vec1, vec2)

Output: 31

This code snippet defines a function sparse_dot_product() that takes two dictionaries as input and calculates the dot product exclusively over the common keys. It’s an efficient solution because it minimizes the number of operations by only considering contributing elements.

Method 2: Using List Comprehension and a Map

Another efficient approach is to perform element-wise multiplication using a list comprehension that pulls values from both vectors where indices match, followed by summing the products using the built-in sum() function.

Here’s an example:

def sparse_dot_product(vec1, vec2):
    return sum(vec1[index] * vec2.get(index, 0) for index in vec1)

# Example Vectors
vec1 = {2: 10, 5: 4}
vec2 = {2: 3, 3: 8}

# Calculate Dot Product
result = sparse_dot_product(vec1, vec2)

Output: 30

Here, the function sparse_dot_product() utilizes a list comprehension to multiply values and dict.get method to handle missing keys by returning 0. This ensures we only process non-zero contributions to the dot product.

Method 3: Using the collections.Counter Class

The Counter class from Python’s collections module simplifies the accumulation of counts and can be adapted for dot product calculation of two sparse representations.

Here’s an example:

from collections import Counter

def sparse_dot_product(vec1, vec2):
    return sum((Counter(vec1) & Counter(vec2)).values())

# Example Vectors
vec1 = Counter({0: 9, 3: 1})
vec2 = Counter({1: 8, 3: 3, 4: 5})

# Calculate Dot Product
result = sparse_dot_product(vec1, vec2)

Output: 3

The Counter objects allow for the easy calculation of dot products by finding the intersection of two vectors — effectively the common indices — and summing their products. The intersection operation & takes care of eliminating non-contributing elements.

Method 4: Using NumPy for High-Dimensional Sparse Vectors

For high-dimensional sparse vectors, NumPy’s support for arrays and efficient numerical computations can come in handy. The method uses dense arrays and should only be used when memory isn’t a limitation.

Here’s an example:

import numpy as np

def sparse_dot_product(vec1, vec2, n):
    # Initialize arrays of length n, filled with zeros
    d_vec1 = np.zeros(n)
    d_vec2 = np.zeros(n)
    # Set the nonzero entries
    for index in vec1:
        d_vec1[index] = vec1[index]
    for index in vec2:
        d_vec2[index] = vec2[index]
    # Calculate the dot product
    return np.dot(d_vec1, d_vec2)

# Example Vectors
vec1 = {0: 3, 3: 7, 8: 2}
vec2 = {0: 5, 2: 6, 8: 4}
n = 10  # Size of the vectors

# Calculate Dot Product
result = sparse_dot_product(vec1, vec2, n)

Output: 23

This code converts sparse vectors into NumPy arrays by inserting their non-zero entries and then calculates the dot product using NumPy’s np.dot() function. This is useful for dense operations but can be memory-intensive for truly large sparse vectors.

Bonus One-Liner Method 5: Functional Approach with map and reduce

The functional programming paradigm provides a concise way to calculate the dot product using map to multiply pairs of values from the vectors and reduce to sum them, implemented in a one-liner.

Here’s an example:

from functools import reduce

sparse_dot_product = lambda vec1, vec2: reduce(lambda x, y: x + y, map(lambda k: vec1.get(k, 0) * vec2.get(k, 0), set(vec1) | set(vec2)), 0)

# Example Vectors
vec1 = {2: 4, 7: 5}
vec2 = {7: 3, 10: 2}

# Calculate Dot Product
result = sparse_dot_product(vec1, vec2)

Output: 15

This one-liner defines a lambda function that takes advantage of map and reduce to compute the dot product of two sparse vectors. It combines both keys and fills in missing ones with zeros, making it a neat and compact solution, though somewhat less readable.


  • Method 1: Dictionary Intersection. Strengths: Direct, efficient, readable. Weaknesses: Potentially less performant with non-optimized dictionary operations.
  • Method 2: List Comprehension with Map. Strengths: Clean and easily readable. Weaknesses: May not be optimal for very large and high-dimensional vectors due to explicit iteration.
  • Method 3: Using collections.Counter. Strengths: Elegant, leverages standard library. Weaknesses: Overhead of converting dictionaries to Counters.
  • Method 4: Using NumPy Arrays. Strengths: Utilizes powerful NumPy library, good for dense operations. Weaknesses: Not memory-efficient for large sparse vectors.
  • Bonus Method 5: Functional One-Liner. Strengths: Compact and requires no explicit definition of functions. Weaknesses: Readability can be poor, not straightforward for those unfamiliar with functional programming concepts.