Hi,
I know it sounds stupid but where is the code for ``as_float_array``?
because of:
from ..utils import array2d, as_float_array, safe_asarray
I was looking in the utils folder, but I can't find it?
Best,
Immanuel
2012/8/16 Alexandre Gramfort <[email protected]>
> hi Immanuel,
>
> yes centering the data can take some time and as_float_array returns a
> C-contiguous
> array with copy=True
>
> What I would do is to add support for order in as_float_array so we avoid
> an
> unnecessary copy.
>
> nice catch !
>
> Alex
>
> On Thu, Aug 16, 2012 at 12:06 PM, iBayer <[email protected]> wrote:
> > 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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