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 <hmf@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 _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
