Hi all,
Some days ago I was trying to train a MKL object by using sparse features.
Different issues have occurred. This time, I saved and loaded a sparse
matrix containing dummy data for training a MKLRegression object.
I could create Sparse feature objects successfully. However, when I call
train() method of the MKLRegression object an error is given. See the code
snippet below for the CauchyKernel():
In [2]: from modshogun import *
In [3]: from scipy.io import mmread
In [4]: from tools.load import LoadMatrix
In [5]: sci_train_data_x =
mmread('sparse_train.mtx').asformat('csr').astype('float64')
In [6]: feats_train = SparseRealFeatures(sci_train_data_x)
In [8]: lm = LoadMatrix()
In [9]: labels_tr =
RegressionLabels(lm.load_labels('labelSparse_train.mtx'))
In [10]: k0 = PolyKernel(10,3)
In [11]: dist = SparseEuclideanDistance(feats_train,feats_train)
In [12]: k1 = CauchyKernel(0, 10, dist)
In [13]: k0.init(feats_train,feats_train)
Out[13]: True
In [14]: k1.init(feats_train,feats_train)
Out[14]: True
In [15]: k = CombinedKernel()
In [16]: k.append_kernel(k0)
Out[16]: True
In [17]: k.append_kernel(k1)
Out[17]: True
In [18]: k.init(feats_train,feats_train)
Out[18]: True
In [19]: mkl = MKLRegression()
In [20]: mkl.set_C(1,1)
In [21]: mkl.set_mkl_norm(2)
In [22]: mkl.set_kernel(k)
In [23]: mkl.set_labels(labels_tr)
In [24]: mkl.train()
---------------------------------------------------------------------------
SystemError Traceback (most recent call last)
<ipython-input-24-ba4aba7733e3> in <module>()
----> 1 mkl.train()
SystemError: [ERROR] In file
/home/iarroyof/shogun/src/shogun/features/SparseFeatures.cpp line 408:
assertion bvec.features failed in float64_t
shogun::CSparseFeatures<ST>::compute_squared_norm(shogun::CSparseFeatures<double>*,
float64_t*, int32_t, shogun::CSparseFeatures<double>*, float64_t*, int32_t)
[with ST = double; float64_t = double; int32_t = int] file
/home/iarroyof/shogun/src/shogun/features/SparseFeatures.cpp line 408
Up to I can observe by going in the code where ASSERTion is placed, it
seems to be there is a type disagreement or probably the ASSERT method does
not find data for computing distance. Any way, I don't know how to fix this
error. Furthermore, a warning is given for other kernels like Gaussian or
Wave. In these cases, no any distance is explicitly computed:
/usr/lib/python2.7/dist-packages/numpy/core/_methods.py:55: RuntimeWarning:
Mean of empty slice.
warnings.warn("Mean of empty slice.", RuntimeWarning)
/usr/lib/python2.7/dist-packages/numpy/core/_methods.py:67: RuntimeWarning:
invalid value encountered in double_scalars
ret = ret.dtype.type(ret / rcount)
In some part of the Shogun class reference I saw SparseRealKernel and I'm
wondering if using common kernels (maybe exclusively dense kernels) is not
compatible with sparse features.
Thank you very much in advance for your help.
--
*Ignacio Arroyo-Fernández*
