Np sparse dot This is a python package for computing the multiplication of two sparse matrices and getting the top-n per row - ymwdalex/sparse_dot_topn def tensordot(a, b, axes=2, *, return_type=None): """ Perform the equivalent of [`numpy. Jul 1, 2021 · Hi everyone, I found that there is no adequate dot function that handles multiplication of dense and sparse matrices such as dot (dense, sparse). csr_matrix. ndarray object using the method dot: scipy. BTW, sparse Apr 22, 2016 · I have two sparse matrices (a and b) in python of the following dimensions: a = <240760x2177930 sparse matrix of type '<class 'numpy. csr_matrix object and contract it with a np. Feb 7, 2014 · The interfaces of numpy. If you inspect on small scale you can see the problem first hand: Dec 21, 2022 · import numpy as np y = np. sparse are almost the same, which is convenient for writing duck-typed code that depends only on the intersection of their interfaces, but here is an exampl Jul 13, 2013 · Given a sparse matrix listing, what's the best way to calculate the cosine similarity between each of the columns (or rows) in the matrix? I would rather not iterate n-choose-two times. The tensordot differs from dot and matmul in that any axis can be chosen for each of the first and second array and the sum of the products is computed just like for matrix multiplication, only not just for the rows of the first times the columns of the second. float64'>' with 1127853 stored elements in Compres >>> import numpy as np >>> from scipy. Here is what I have tried so far: Scipy Sparse Format: Naturally, I converted the numpy sparse matrix to scipy csr_matrix. 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 Warning As of NumPy 1. numpy. dot(coo_array, coo_array), numpy treats it as a dot product between two scalars object arrays of type coo_array. See Also numpy. dot(v) array([ 1, -3, -1], dtype=int64) For 2-D arrays it is the matrix product: Jan 5, 2025 · Learn how to perform sparse matrix operations using SciPy with practical examples. One of its most powerful and frequently used functions is `np. A dot product between two scalars is the same as multiplying those scalars, so this effectively resolves to coo_array * coo_array Mar 6, 2024 · Problem Formulation: In numerical computing, calculating the dot product of two sparse vectors efficiently is crucial for performance. Oct 10, 2017 · That np. This casting behavior is a decision made by the scipy. Warning This function returns a sparse matrix – not a sparse array. In 1. Change the function get_matches_df with this: def get_matches_df(sparse_matrix, A, B numpy. Using scipy's csr_matrix, this appears to be significantly slower than using The tensordot differs from dot and matmul in that any axis can be chosen for each of the first and second array and the sum of the products is computed just like for matrix multiplication, only not just for the rows of the first times the columns of the second. Comparing very large feature vectors and picking the best matches, in practice often results in performing a sparse matrix multiplication followed by selecting the top-n multiplication results. There is also a suggestion to convert sparse to dense first in order to multiply like so, se Warning This function returns a sparse matrix – not a sparse array. dot`. Sparse vectors are those with a majority of zero elements, commonly found in data science and machine learning. The present document can be seen both as a brief lesson on sparse matrices, but sparse. 18 we have several Jul 24, 2023 · In what follows, we will introduce the smn (Sparse Matrices in Numba) library that can be used in conjunction with Numba to handle sparse matrices – meaning can be used inside functions compiled through Numba – providing an effective solution for scientific computing tasks that involve sparse data structures. dot` function is used for performing dot products, which are fundamental operations in linear algebra. e. Ideal for beginners in Python and scientific computing. dot : Equivalent function for COO objects. Here I introduce the core concepts of the spDMD and provide a rudimentary implementation in Python. dot(a, b, out=None) # Dot product of two arrays. Computing time: Computing time can be saved by logically designing a data structure traversing only non-zero Nov 20, 2015 · I have a situation in which I need to extract a single row from a sparse matrix and take its dot product with a dense row. tensordot`][]. However, task is still getting killed due to memory issue. sparse. Running a sparse_dot_topn function is giving me Warning: Kernel Restarted? Asked 4 years, 9 months ago Modified 4 years, 2 months ago Viewed 962 times Oct 16, 2025 · In the world of scientific computing and data analysis in Python, NumPy stands as a cornerstone library. np. dot # numpy. These matrices are then multiplied element-wise using the multiply () method focusing only on non-zero elements. Comparing very large feature vectors and picking the best matches, in practice often results in performing sparse matrix multiplication followed by selecting the top-n multiplication results. Jul 5, 2025 · If most of the elements of the matrix have 0 value, then it is called a sparse matrix. Depending on what BLAS library your version of numpy is linked to, these are often multithreaded and very heavily optimised. sparse import csr_array >>> A = csr_array([[1, 2, 0], [0, 0, 3], [4, 0, 5]]) >>> v = np. The lil_array format is row-based, so conversion to CSR is efficient, whereas conversion to CSC is less so. Why not just convert to array and then use dot : B. b (Union [SparseArray, np. Jun 23, 2022 · I'd like to perform a matrix product of two sparse SciPy matrices. dot is sparse aware, though I don't know the details of how it acts on that. The Compressed Sparse Row (CSR) format is particularly efficient for row slicing and matrix-vector products. Is there any possibility to parallelize the sparse matrix operations (like dot/kron) in scipy? numpy. Matrix vector product # To do a vector product between a 2D sparse array and a vector use the matmul operator (i. ndarray). Sparse_dot_topn provides a fast way to performing a sparse matrix multiplication followed by top-n multiplication result selections. warn ( "Set debug mode with sparse_dot_mkl. You are encouraged to use diags_array to take advantage of the sparse array functionality. random. Parameters ---------- a, b : Union[SparseArray, np. Understanding these formats especially important for effectively using sparse matrices in your applications. We want to use the sparse properties of the matrix A to optimize the dot product, i. dot(M, M, out) for preallocated out. Returns: The result of the operation. csr_dot is single-threaded, and as your timings show, only shows efficiency gains when dealing with highly sparse matrices. dot(a, b)[source] Perform the equivalent of numpy. py >>> import numpy as np >>> from scipy. Return type: Union [SparseArray, numpy. You are encouraged to use dia_array to take advantage of the sparse array functionality. However, the result is not sparse, in my case, so I'd like to store it as a dense NumPy array. COO. It uses some clever optimization tricks to try to reconstruct the original data with as few DMD modes as possible. >>> import numpy as np >>> from scipy. dot assumes the inputs are numpy arrays, and scipy sparse matrices are not numpy arrays. By popular demand, the latest np. Oct 18, 2024 · sparse_dot_topn provides a fast way to performing a sparse matrix multiplication followed by top-n multiplication result selection. dot(B,A). sparse implementation (via the __array__ method), likely to avoid unintentional conversion of sparse arrays to full dense arrays. array([1, 0, -1]) >>> A. dot is not aware of sparse matrices, therefore using it will result on unexpected results or errors. Python package to accelerate the sparse matrix multiplication and top-n similarity selection - ing-bank/sparse_dot_topn May 31, 2024 · I am not sure if I am doing something wrong but I am using scipy. sparse offers multiple sparse matrix formats, each suited for specific tasks, and integrates with NumPy through conversions, operations, and shared data types. ndarray Jun 25, 2015 · A sparse matrix is not a numpy array or matrix, though most formats use several arrays to store their data. dot(v) array([ 1 Nov 7, 2012 · The reason the dot product runs into memory issues when computing r = dot (C,Y) is because numpy's dot function does not have native support for handling sparse matrices. Is it possible to do this efficient Mar 1, 2017 · Let us consider a matrix A as a diagonal matrix and a matrix B a random matrix, both with size N x N. What is happening is numpy thinks of the sparse matrix C as a python object, and not a numpy array. ) Jul 5, 2025 · Output Output of Multiplication of Two CSC matrices Example 2: Multiply Two csr_matrix Matrices Here, two sparse matrices are created using csr_matrix () class. . sparse import coo_array >>> A = coo_array([[1, 2, 0], [0, 0, 3], [4, 0, 5]]) >>> v = np. sparse. If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred. I noticed that the operation is not parallelized. Use `sparse_dot_mkl. dot on two arrays. dot (np. Oct 1, 2020 · NAME MATCHING. Jul 22, 2023 · I think this is expected, unfortunately. set_debug_mode (True)", DeprecationWarning ) print_mkl_debug () return _qrs (matrix_a, matrix_b, cast=cast) # Alias for backwards compatibility dot_product_transpose_mkl = gram Python package to accelerate the sparse matrix multiplication and top-n similarity selection - ing-bank/sparse_dot_topn The most common representations include Compressed Sparse Row (CSR), Compressed Sparse Column (CSC), and Coordinate List (COO). (See Notes below. choice([-1,1], size=(600000, 256)) I need dot product of x and y at lowest possible memory required. Speed is not the primary concern. matrix and scipy. The corresponding dense array should be obtained first instead: Dec 18, 2018 · You can import awesome_cossim_top function directly from the sparse_dot_topn lib. As a general rule, regular numpy functions aren't aware of sparse matrices, so you should count on using the sparse versions of functions and operators. So when you call np. ndarray, scipy. Dot products have a wide range of applications, from machine learning algorithms like neural networks to physics Aug 3, 2016 · The sparsity-promoting DMD (spDMD) is motivated by the question of how to find the best modes for a system. toarray()). Jul 28, 2015 · The reason why np. dot : NumPy equivalent function. 7, np. Parameters: a (Union [SparseArray, np. The `np. dot might be forcing an elementwise multiplication rather than matrix-multiplication and hence broadcasting. spmatrix]) – The arrays to perform the dot operation on. dot(v) array([ 1 1 day ago · np. set_debug_mode (True)` :type debug: bool :return: Dense array X :rtype: np. dot is so much faster for small matrices is that it typically calls a BLAS function to do the actual multiplication. , @) which performs a dot product (like the dot method): M*M is the element-wise product, you need to compare sparse dot versus M@M and, as an interesting additional result, against the dense np. ndarray] Raises Apr 26, 2025 · scipy. Specifically, If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation). Source code in sparse/numba_backend/_common. ndarray """ if debug: warnings. dot(A. The two major benefits of using sparse matrix instead of a simple matrix are: Storage: There are lesser non-zero elements than zeros and thus lesser memory can be used to store only those elements. dot of two scalars is equivalent to multiplication, and v * w returns a length 3 sparse array. lcbhf tc4 bx7gw 5iwcj2h apoiy i9eesn lpz5ga5 4onsh eoc 2sguf