Thanks for the pointers.
I am using stable version 0.17 so I used nested cross validation with
different corr types. I could get the best result using linear correlation.
However it was still short of what i got using universal Pearson VII
function based kernel ( as available in weka, off the shelf) for both SVM
and gaussian regression.
Hence I was trying to find if there is an implementation already in
scikit where I can specify puk as kernel option since I was not sure if I
could myself correctly implement the puk kernel.
Thanks,
Amita
On Fri, Apr 1, 2016 at 7:51 AM, Andreas Mueller <t3k...@gmail.com> wrote:
> Upgrading to the dev version might be the better route, though.
>
>
> On 04/01/2016 01:56 AM, Vincent Dubourg wrote:
>
> Hi,
>
> Manoj's tip only works if you are already using the 0.18dev version of
> sklearn that brings a total refactoring of the gaussian_process submodule.
>
> If you are still using the latest stable (aka 0.17), then custom kernels
> can be specified as callable (simple Python function) using the `corr`
> keyword argument.
>
> From GaussianProcess' docstring (v0.17):
>
>> *corr* : string or callable, optional
>>
>> A stationary autocorrelation function returning the autocorrelation
>> between two points x and x’. Default assumes a squared-exponential
>> autocorrelation model. Built-in correlation models are:
>>
>> 'absolute_exponential', 'squared_exponential','generalized_exponential',
>> 'cubic', 'linear'
>>
>>
> Here is the code for the squared_exponential kernel:
>
> def squared_exponential(theta, d):
>> """
>> Squared exponential correlation model (Radial Basis Function).
>> (Infinitely differentiable stochastic process, very smooth)::
>> n
>> theta, dx --> r(theta, dx) = exp( sum - theta_i * (dx_i)^2 )
>> i = 1
>> Parameters
>> ----------
>> theta : array_like
>> An array with shape 1 (isotropic) or n (anisotropic) giving the
>> autocorrelation parameter(s).
>> dx : array_like
>> An array with shape (n_eval, n_features) giving the componentwise
>> distances between locations x and x' at which the correlation
>> model
>> should be evaluated.
>> Returns
>> -------
>> r : array_like
>> An array with shape (n_eval, ) containing the values of the
>> autocorrelation model.
>> """
>>
>> theta = np.asarray(theta, dtype=np.float)
>> d = np.asarray(d, dtype=np.float)
>>
>> if d.ndim > 1:
>> n_features = d.shape[1]
>> else:
>> n_features = 1
>>
>> if theta.size == 1:
>> return np.exp(-theta[0] * np.sum(d ** 2, axis=1))
>> elif theta.size != n_features:
>> raise ValueError("Length of theta must be 1 or %s" % n_features)
>> else:
>> return np.exp(-np.sum(theta.reshape(1, n_features) * d ** 2,
>> axis=1))
>>
>
> Implement the Pearson kernel like this and pass it to the GaussianProcess
> estimator.
>
> Cheers,
> Vincent
>
> 2016-04-01 1:59 GMT+02:00 Manoj Kumar <manojkumarsivaraj...@gmail.com>:
>
>> Hi,
>>
>> I remember seeing it somewhere but don't recall where exactly.
>>
>> You can do it by the following.
>>
>> 1. Inherit from Kernel (
>> http://scikit-learn.org/dev/modules/generated/sklearn.gaussian_process.kernels.Kernel.html#sklearn.gaussian_process.kernels.Kernel
>> )
>> 2. Make your hyperparameters on the kernel an attribute starting with
>> `hyperparameter_` and make it an instance of Hyperparameter as done here (
>> <https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/gaussian_process/kernels.py#L1711>
>> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/gaussian_process/kernels.py#L1711
>> )
>> 3. Implement a __call__ that computes the kernel matrix and if possible
>> the gradient.
>>
>> If not already present, this might be worth illustrating in an example
>>
>>
>> On Thu, Mar 31, 2016 at 7:45 PM, Sebastian Raschka <
>> <se.rasc...@gmail.com>se.rasc...@gmail.com> wrote:
>>
>>> Seems like that the GaussianProcess class only has an autocorrelation
>>> parameter, but when I understand correctly, the autocorrelation function is
>>> just the “normalized” covariance kernel, thus it may be possible to provide
>>> custom kernels here as well? If not, it may be interesting to re-factor it
>>> a little bit and borrow the code from SVM to accept custom kernels in
>>> Gaussian Processes as well?
>>>
>>> > On Mar 31, 2016, at 5:17 PM, Andreas Mueller <t3k...@gmail.com> wrote:
>>> >
>>> > Hi.
>>> > What do you mean by Gaussian regression?
>>> > You can specify your own kernels for SVMs, but it will be a bit slower.
>>> >
>>> > Cheers,
>>> > Andy
>>> >
>>> > On 03/28/2016 09:40 PM, Amita Misra wrote:
>>> >> Hi,
>>> >>
>>> >> I was using weka earlier for support vector regression and gaussian
>>> regression
>>> >>
>>> >> I am now switching to scikit and was trying to replicate my results
>>> using support vector regression.
>>> >> I could get similar results for poly and rbf kernel.
>>> >>
>>> >> Is there a way I can specify Pearson VII function kernel(Puk Kernel)
>>> for support vector and Gaussian regression?
>>> >>
>>> >> Thanks,
>>> >> Amita
>>> >>
>>> >>
>>> >>
>>> >> --
>>> >> Amita Misra
>>> >> Graduate Student Researcher
>>> >> Natural Language and Dialogue Systems Lab
>>> >> Baskin School of Engineering
>>> >> University of California Santa Cruz
>>> >>
>>> >>
>>> >>
>>> >>
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>>> >>
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>>> >>
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>>> >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
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>>
>>
>>
>> --
>> Manoj,
>> http://github.com/MechCoder
>>
>>
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>>
>
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>
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--
Amita Misra
Graduate Student Researcher
Natural Language and Dialogue Systems Lab
Baskin School of Engineering
University of California Santa Cruz
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