I did something similar where I was using GridSearchCV over different
kernel functions for SVM and not all kernel functions use the same
parameters. For example, the *degree* parameter is only used by the *poly*
kernel.
from sklearn import svm
from sklearn import cross_validation
from sklearn import grid_search
params =
[{'kernel':['poly'],'degree':[1,2,3],'gamma':[1/p,1,2],'coef0':[-1,0,1]},\
{'kernel':['rbf'],'gamma':[1/p,1,2],'degree':[3],'coef0':[0]},\
{'kernel':['sigmoid'],'gamma':[1/p,1,2],'coef0':[-1,0,1],'degree':[3]}]
GSC = grid_search.GridSearchCV(estimator = svm.SVC(), param_grid = params,\
cv = cvrand, n_jobs = -1)
This worked in this instance because the svm.SVC() object only passes
parameters to the kernel functions as needed:
[image: Inline image 1]
Hence, even though my list of dicts includes all three parameters for all
types of kernels I used, they were selectively ignored. I'm not sure about
parameters for the distance metrics for the KNN object, but it's a good bet
it works the same way.
Andrew
<~~~~~~~~~~~~~~~~~~~~~~~~~~~>
J. Andrew Howe, PhD
Editor-in-Chief, European Journal of Mathematical Sciences
Executive Editor, European Journal of Pure and Applied Mathematics
www.andrewhowe.com
http://www.linkedin.com/in/ahowe42
https://www.researchgate.net/profile/John_Howe12/
I live to learn, so I can learn to live. - me
<~~~~~~~~~~~~~~~~~~~~~~~~~~~>
On Mon, Jun 27, 2016 at 1:27 PM, Hugo Ferreira <h...@inesctec.pt> wrote:
> Hello,
>
> I have posted this question in Stackoverflow and did not get an answer.
> This seems to be a basic usage question and am therefore sending it here.
>
> I have following code snippet that attempts to do a grid search in which
> one of the grid parameters are the distance metrics to be used for the KNN
> algorithm. The example below fails if I use "wminkowski", "seuclidean" or
> "mahalanobis" distances metrics.
>
> # Define the parameter values that should be searched
> k_range = range(1,31)
> weights = ['uniform' , 'distance']
> algos = ['auto', 'ball_tree', 'kd_tree', 'brute']
> leaf_sizes = range(10, 60, 10)
> metrics = ["euclidean", "manhattan", "chebyshev", "minkowski",
> "mahalanobis"]
>
> param_grid = dict(n_neighbors = list(k_range), weights = weights,
> algorithm = algos, leaf_size = list(leaf_sizes), metric=metrics)
> param_grid
>
> # Instantiate the algorithm
> knn = KNeighborsClassifier(n_neighbors=10)
>
> # Instantiate the grid
> grid = GridSearchCV(knn, param_grid=param_grid, cv=10, scoring='accuracy',
> n_jobs=-1)
>
> # Fit the models using the grid parameters
> grid.fit(X,y)
>
> I assume this is because I have to set or define the ranges for the
> various distance parameters (for example p, w for “wminkowski” -
> WMinkowskiDistance ). The "minkowski" distance may be working because its
> "p" parameter has the default 2.
>
> So my questions are:
>
> 1. Can we set the range of parameters for the distance metrics for the
> grid search and if so how?
> 2. Can we set the value of a parameters for the distance metrics for the
> grid search and if so how?
>
> Hope the question is clear.
> TIA
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