Hi List,
I'm currently working on some performance documentation [1] and I wanted to
micro-benchmark the dense vs sparse coefficients case.
I created a self-contained script and wanted to bench it using
line_profiler, but it seems that after the call to `sparsify()` my
SGDRegressor can't predict anymore (crashes with a dimensions mismatch
error).
Here is a gist to reproduce that: [2].
The weird thing is that the coeffs_ attribute changes shape after the call
to sparsify:
(30,) -> (1, 30)
where 30 equals to n_features in my case.
Any idea or explanation welcome !
[1] https://github.com/scikit-learn/scikit-learn/pull/2488
[2] https://gist.github.com/oddskool/7300982
PS: The stack trace:
"""
Traceback (most recent call last):
File "/usr/local/bin/kernprof.py", line 233, in <module>
sys.exit(main(sys.argv))
File "/usr/local/bin/kernprof.py", line 221, in main
execfile(script_file, ns, ns)
File "sparsity_benchmark.py", line 52, in <module>
score(y_test, clf.predict(X_test), 'sparse model')
File
"/usr/local/lib/python2.7/dist-packages/sklearn/linear_model/stochastic_gradient.py",
line 903, in predict
return self.decision_function(X)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/linear_model/stochastic_gradient.py",
line 888, in decision_function
scores = safe_sparse_dot(X, self.coef_) + self.intercept_
File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/extmath.py",
line 190, in safe_sparse_dot
ret = a * b
File "/usr/lib/python2.7/dist-packages/scipy/sparse/base.py", line 311,
in __rmul__
return (self.transpose() * tr).transpose()
File "/usr/lib/python2.7/dist-packages/scipy/sparse/base.py", line 278,
in __mul__
raise ValueError('dimension mismatch')
ValueError: dimension mismatch
"""
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