I have a pandas dataframe like user_id music_id has_rating A a 1 B b 1 and I would like to automatically add new rows for each of user_id & music_id for those users haven’t rated, like user_id music_id has_rating A a 1 A b 0 B a 0 B b 1 for each of user_id and music_id combination pairs those
Tag: sparse-matrix
Creating adjacency matrix from sparse SKU data in Python
I have ecommerce data with about 6000 SKUs and 250,000 obs. Simple version below but a lot more sparse. There is only one SKU per line as each line is a transaction. What I have: I want to create a weighted undirected adjacency matrix so that I can do some graph analysis on the market baskets. It would look like
Import large .tiff file as sparse matrix
I have a large .tiff file (4.4gB, 79530 x 54980 values) with 1 band. Since only 16% of the values are valid, I was thinking it’s better to import the file as sparse matrix, to save RAM. When I first open it as np.array and then transform it into a sparse matrix using csr_matrix(), my kernel already crashes. See code
How do I construct an incidence matrix from two dataframe columns using scipy.sparse.coo_matrix((data, (i, j)))?
I have a pandas DataFrame containing two columns [‘A’, ‘B’]. Each column is made up of integers. I want to construct a sparse matrix with the following properties: row index is all integers from 0 to the max value in the dataframe column index is the same as row index entry i,j = 1 if [i,j] or [j,i] is a
score calculation takes too long: avoid for loops – python
I am new to python and I need your kindly help. I have three matrices, in particular: Matrix M (class of the matrix: scipy.sparse.csc.csc_matrix), dimensions: N x C; Matrix G (class of the matrix: numpy.ndarray), dimensions: C x T; Matrix L (class of the matrix: numpy.ndarray), dimensions: T x N. Where: N = 10000, C = 1000, T = 20.
Compute sum of power of large sparse matrix
Given a query vector (one-hot-vector) q with size of 50000×1 and a large sparse matrix A with size of 50000 x 50000 and nnz of A is 0.3 billion, I want to compute r=(A + A^2 + … + A^S)q (usually 4 <= S <=6). I can above equation iteratively using loop but I want to more fast method. First
Trying to make a graph from a sparse matrix: not enough values to unpack (expected 2, got 0)
So I’m trying to make a graph with squares that are colored according to probability densities stored in the 7×7 matrix ‘nprob’. I get the following error: To be honest, I get this error a lot, and I usually just rejuggle things until I get one I understand better, so it’s probably about time to learn what it means. What
Initialize high dimensional sparse matrix
I want to initialize 300,000 x 300,0000 sparse matrix using sklearn, but it requires memory as if it was not sparse: it gives the error: which is the same error as if I initialize using numpy: Even when I go to a very low density, it reproduces the error: Is there a more memory-efficient way to create such a sparse
How to convert a PyTorch sparse_coo_tensor into a PyTorch dense tensor?
I create a sparse_coo tensor in PyTorch: Now I want to convert a PyTorch sparse tensor into a PyTorch dense tensor. Which function can be used? Answer you can use to_dense as suggested in this example : And by the way, the documentation is here
Python matrix multiplication: sparse multiply dense
Given the code snippet: where A is a CSR scipy sparse matrix, M and T are two numpy arrays. Question: During the matrix operations, does numpy treat A as a dense matrix, or M and T as two sparse matrices? I suspect that the latter case is true since the resulting matrix B is not in the sparse format. I