Python MemoryError, wenn dabei die Montage mit Scikit-learn

Ich verwende Python 2.7 (64-bit) auf eine Windows 8 64-bit-system mit 24GB Speicher. Bei der Montage der üblichen Sklearn.linear_models.Ridge, der code läuft einwandfrei.

Problem: Allerdings, wenn Sie Sklearn.linear_models.RidgeCV(alphas=alphas) für den Einbau, ich Lauf in den MemoryError Fehler unten auf der Linie rr.fit(X_train, y_train) führt, dass die fitting-Prozedur.

Wie kann ich verhindern, dass dieser Fehler?

Code-snippet

def fit(X_train, y_train):
    alphas = [1e-3, 1e-2, 1e-1, 1e0, 1e1]

    rr = RidgeCV(alphas=alphas)
    rr.fit(X_train, y_train)

    return rr


rr = fit(X_train, y_train)

Fehler

MemoryError                               Traceback (most recent call last)
<ipython-input-41-a433716e7179> in <module>()
      1 # Fit Training set
----> 2 rr = fit(X_train, y_train)

<ipython-input-35-9650bd58e76c> in fit(X_train, y_train)
      3 
      4     rr = RidgeCV(alphas=alphas)
----> 5     rr.fit(X_train, y_train)
      6 
      7     return rr

C:\Python27\lib\site-packages\sklearn\linear_model\ridge.pyc in fit(self, X, y, sample_weight)
    696                                   gcv_mode=self.gcv_mode,
    697                                   store_cv_values=self.store_cv_values)
--> 698             estimator.fit(X, y, sample_weight=sample_weight)
    699             self.alpha_ = estimator.alpha_
    700             if self.store_cv_values:

C:\Python27\lib\site-packages\sklearn\linear_model\ridge.pyc in fit(self, X, y, sample_weight)
    608             raise ValueError('bad gcv_mode "%s"' % gcv_mode)
    609 
--> 610         v, Q, QT_y = _pre_compute(X, y)
    611         n_y = 1 if len(y.shape) == 1 else y.shape[1]
    612         cv_values = np.zeros((n_samples * n_y, len(self.alphas)))

C:\Python27\lib\site-packages\sklearn\linear_model\ridge.pyc in _pre_compute_svd(self, X, y)
    531     def _pre_compute_svd(self, X, y):
    532         if sparse.issparse(X) and hasattr(X, 'toarray'):
--> 533             X = X.toarray()
    534         U, s, _ = np.linalg.svd(X, full_matrices=0)
    535         v = s ** 2

C:\Python27\lib\site-packages\scipy\sparse\compressed.pyc in toarray(self, order, out)
    559     def toarray(self, order=None, out=None):
    560         """See the docstring for `spmatrix.toarray`."""
--> 561         return self.tocoo(copy=False).toarray(order=order, out=out)
    562 
    563     ##############################################################

C:\Python27\lib\site-packages\scipy\sparse\coo.pyc in toarray(self, order, out)
    236     def toarray(self, order=None, out=None):
    237         """See the docstring for `spmatrix.toarray`."""
--> 238         B = self._process_toarray_args(order, out)
    239         fortran = int(B.flags.f_contiguous)
    240         if not fortran and not B.flags.c_contiguous:

C:\Python27\lib\site-packages\scipy\sparse\base.pyc in _process_toarray_args(self, order, out)
    633             return out
    634         else:
--> 635             return np.zeros(self.shape, dtype=self.dtype, order=order)
    636 
    637 

MemoryError: 

---

Code

print type(X_train)
print X_train.shape

Ergebnis

<class 'scipy.sparse.csr.csr_matrix'>
(183576, 101507)