import numpy as np
import scipy.sparse as sp
from pysindy import PolynomialLibrary
x = sp.csr_matrix([[2,3]])
lib = PolynomialLibrary(degree=2, include_bias=False, include_interaction=False)
y = lib.fit_transform(x)
expected = np.array([[2,3,4,9]])
result = y.toarray()
print(result)
array([[2, 3, 4, 6, 9]])
Notes:
Found while troubleshooting #365
Our PolynomialLibrary differs from scikit-learn's PolynomialFeatures only in allowing the include_interaction parameter. However, in order to take advantage of fast polynomial features on sparse inputs, we were calling their compiled cython function _csr_polynomial_expansion(). However, that doesn't allow an include_interaction parameter.
The solution is to treat CSR matrices just like any other matrices. It won't be as fast in the extremely limited case where we're using a CSR matrix, low-degree polynomials, and include_interactions=True. See #223. I will also modify test_polynomial_options to check that library.powers_ matches the shape from fit_transform()
PySINDy/Python version information:
master and all recent releases, but requires scikit-learn<1.3 or else another error appears.
Reproducing code example:
Notes:
Found while troubleshooting #365
Our
PolynomialLibrarydiffers from scikit-learn'sPolynomialFeaturesonly in allowing theinclude_interactionparameter. However, in order to take advantage of fast polynomial features on sparse inputs, we were calling their compiled cython function_csr_polynomial_expansion(). However, that doesn't allow aninclude_interactionparameter.The solution is to treat CSR matrices just like any other matrices. It won't be as fast in the extremely limited case where we're using a CSR matrix, low-degree polynomials, and
include_interactions=True. See #223. I will also modifytest_polynomial_optionsto check thatlibrary.powers_matches the shape fromfit_transform()PySINDy/Python version information:
masterand all recent releases, but requires scikit-learn<1.3 or else another error appears.