Hi all,
Is the KernelDensity estimator compatible with pipelines? When I try
to use it inside one
pipe1 = make_pipeline(StandardScaler(with_mean=True, with_std=True),
KernelDensity(algorithm="auto",
kernel="gaussian", metric="euclidean"))
params = dict(kerneldensity__bandwidth=np.logspace(-10, 1, 100))
search = GridSearchCV(pipe1, param_grid=params, verbose=1, n_jobs=8, cv=5)
search.fit(feats1)
search.best_estimator_
I get a TypeError as follows:
/home/desouza/anaconda/lib/python2.7/site-packages/sklearn/pipeline.pyc
in fit(self=Pipeline(steps=[('standardscaler',
StandardScale...euclidean',
metric_params=None, rtol=0))]), X=array([[ 5.701 ,
73.6443 , 61.7018 ...2.7188 ,
0.18243243, 0.21621622]]), y=None, **fit_params={})
125 def fit(self, X, y=None, **fit_params):
126 """Fit all the transforms one after the other and transform the
127 data, then fit the transformed data using the final estimator.
128 """
129 Xt, fit_params = self._pre_transform(X, y, **fit_params)
--> 130 self.steps[-1][-1].fit(Xt, y, **fit_params)
131 return self
132
133 def fit_transform(self, X, y=None, **fit_params):
134 """Fit all the transforms one after the other and transform the
TypeError: fit() takes exactly 2 arguments (3 given)
Is this an issue or it is supposed not to be compatible? A quick
search in the mailing list and on stackoverflow did not return any
entry about this.
Thanks,
José
On Tue, Oct 21, 2014 at 3:03 PM, Jacob Vanderplas
<jake...@cs.washington.edu> wrote:
> Hi Jose,
> The KDE implementation does work on multivariate data, and will in general
> work for multimodal data as well. There are two caveats to that:
>
> 1. In the sklearn implementation, the bandwidth must be the same across each
> dimension. If this poses a problem for your data, the data can be scaled
> before the fit (Using StandardScaler or something similar).
> 2. The results will depend strongly on the choice of bandwidth: it's
> important to cross-validate to determine the optimal bandwidth, as is done
> in
> http://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html
>
> Good luck!
> Jake
>
>
> Jake VanderPlas
> Director of Research – Physical Sciences
> eScience Institute, University of Washington
> http://www.vanderplas.com
>
> On Tue, Oct 21, 2014 at 2:09 AM, José Guilherme Camargo de Souza
> <jose.camargo.so...@gmail.com> wrote:
>>
>> Hi all,
>>
>> I would like to ask if the density estimation implementation of scikit
>> works with multivariate multimodal data. In the digits example [1] it
>> is clear that it supports multivariate datasets and in the guide
>> description [2] a 1-D bimodal distribution is used.
>>
>> Is it possible to use the same implementation on multivariate
>> gaussian-shaped data with more than 2 modes? If so, are there any
>> shortcomings or useful tips when doing that?
>>
>> Thanks in advance,
>> José
>>
>> [1]
>> http://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html#example-neighbors-plot-digits-kde-sampling-py
>> [2]
>> http://scikit-learn.org/stable/modules/density.html#kernel-density-estimation
>> José Guilherme
>>
>>
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