Hello,
I just got the impression that linear_model.base.center_data always returns
a C_CONTIGUOUS array even if
it has been made f-continous before X = np.asfortranarray(X) (see ipython
session below). It looks to me that this causes some expensive
memory operation when fitting some models (see line profile below)
Am I missing something here?
best,
Immanuel
-----
In [10]: from sklearn.linear_model.base import center_data
In [12]: from sklearn.datasets.samples_generator import make_regression
In [13]: %paste
X, y, coef = make_regression(n_samples=10000, n_features=5000,
n_informative=1000,
random_state=0, coef=True)
X = np.asfortranarray(X)
## -- End pasted text --
In [14]: X.flags
Out[14]:
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
In [17]: %paste
X, y, X_mean, y_mean, X_std = center_data(X, y,
fit_intercept=False, normalize=False, copy=True)
## -- End pasted text --
In [18]: X.flags
Out[18]:
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
----
---
Timer unit: 1e-06 s
File:
/home/mane/git/enet_strong_rules/sklearn/linear_model/coordinate_descent.py
Function: _dense_fit at line 167
Total time: 7.60257 s
Line # Hits Time Per Hit % Time Line Contents
==============================================================
167 def _dense_fit(self,
X, y, Xy=None, coef_init=None,
168
active_set_init=None, alpha_init=None, R_init=None):
177 1 2 2.0 0.0 X, y, X_mean,
y_mean, X_std = self._center_data(X, y,
178 1 4457984 4457984.0 58.6
self.fit_intercept, self.normalize,
copy=self.copy_X)
200 1 1273583 1273583.0 16.8 X =
np.asfortranarray(X) # make data contiguous in memory
213 1 5 5.0 0.0
self._fit_enet_with_strong_rule(X, y, Xy,
214 1 4 4.0 0.0
active_set_init=active_set_init, coef_init=coef_init,
215 1 1867135 1867135.0
24.6 alpha_init=alpha_init,
R_init=R_init)
----
------------------------------------------------------------------------------
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