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sparse_data = sparse.csr_matrix(data, shape=shape) |
Hi, I’d like to suggest a minor optimization based on the internal implementation of sparse-to-dense conversions.
Current pattern:
sparse_data = sparse.csr_matrix(data, shape=shape)
np_values = np.squeeze(np.asarray(sparse_data.todense()))
Suggested replacement:
np_values = np.squeeze(sparse_data.toarray())
-
sparse.todense() returns a numpy.matrix object.
This is a legacy type in NumPy with different operator semantics (* means matrix multiplication, not element-wise). While np.asarray(...) wraps it, it doesn’t eliminate matrix-specific behaviors unless explicitly copied or converted, which introduces subtle performance and correctness risks.
-
np.asarray(...) adds unnecessary overhead.
Calling np.asarray on a numpy.matrix still requires type checking and often incurs an additional function call stack. It doesn’t simplify the type structure, but merely suppresses copying. This leads to a verbose and slightly less performant pipeline.
-
sparse.toarray() is the clean, modern alternative.
It directly creates a numpy.ndarray from the sparse structure using C-level memory allocation, avoiding the intermediate matrix object entirely. Internally, this path is optimized and aligned with NumPy’s preferred dense representation.
naeural_core/naeural_core/utils/predictive_analytics/timeseries/numpyize_time_series.py
Line 1626 in d68c251
Hi, I’d like to suggest a minor optimization based on the internal implementation of sparse-to-dense conversions.
Current pattern:
Suggested replacement:
np_values = np.squeeze(sparse_data.toarray())sparse.todense() returns a numpy.matrix object.
This is a legacy type in NumPy with different operator semantics (* means matrix multiplication, not element-wise). While np.asarray(...) wraps it, it doesn’t eliminate matrix-specific behaviors unless explicitly copied or converted, which introduces subtle performance and correctness risks.
np.asarray(...) adds unnecessary overhead.
Calling np.asarray on a numpy.matrix still requires type checking and often incurs an additional function call stack. It doesn’t simplify the type structure, but merely suppresses copying. This leads to a verbose and slightly less performant pipeline.
sparse.toarray() is the clean, modern alternative.
It directly creates a numpy.ndarray from the sparse structure using C-level memory allocation, avoiding the intermediate matrix object entirely. Internally, this path is optimized and aligned with NumPy’s preferred dense representation.