Scipy doesn’t have a way to do this without copying the data but you can do it yourself by changing the attributes that define the sparse matrix. There are 4 attributes that make up the csr_matrix: data: An array containing the actual values in the matrix indices: An array containing the column index corresponding to … Read more
This has been implemented in scikit-learn sklearn.preprocessing.normalize. from sklearn.preprocessing import normalize w_normalized = normalize(w, norm=’l1′, axis=1) axis=1 should normalize by rows, axis=0 to normalize by column. Use the optional argument copy=False to modify the matrix in place.
A direct conversion is not supported ATM. Contributions are welcome! Try this, should be ok on memory as the SpareSeries is much like a csc_matrix (for 1 column) and pretty space efficient In [37]: col = np.array([0,0,1,2,2,2]) In [38]: data = np.array([1,2,3,4,5,6],dtype=”float64″) In [39]: m = csc_matrix( (data,(row,col)), shape=(3,3) ) In [40]: m Out[40]: <3×3 … Read more
It’s a tough one without reviewing your whole project. Here are couple things you can do… 1) check to see if you are preprocessing the dataset and splitting it into training and testing sets in the same way as described in the paper. differences in data preprocessing and splitting can have a significant impact on … Read more
A sparse matrix is constructed from regular numpy arrays, so you can get the byte count for any of these just as you would a regular array. If you just want the number of bytes of the array elements: >>> from scipy.sparse import csr_matrix >>> a = csr_matrix(np.arange(12).reshape((4,3))) >>> a.data.nbytes 88 If you want the … Read more
Looking into the source via the link you provided, TruncatedSVD is basically a wrapper around sklearn.utils.extmath.randomized_svd; you can manually call this yourself like this: from sklearn.utils.extmath import randomized_svd U, Sigma, VT = randomized_svd(X, n_components=15, n_iter=5, random_state=None)
Here is a method that creates a sparse scipy matrix based on data and indices of person and thing. person_u and thing_u are lists representing the unique entries for your rows and columns of pivot you want to create. Note: this assumes that your count column already has the value you want in it. from … Read more
The sparse library now has hstack and vstack for respectively concatenating matrices horizontally and vertically.
Sorry if I’m not answering this completely enough, but hopefully I can provide some insight. CSC (Compressed Sparse Column) and CSR (Compressed Sparse Row) are more compact and efficient, but difficult to construct “from scratch”. Coo (Coordinate) and DOK (Dictionary of Keys) are easier to construct, and can then be converted to CSC or CSR … Read more