Yeah I know :-). I did it like that for a specific reason which I no longer remember :-D. But, you know, it was probably a good one...hahaha
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 2:37 PM, Joel Nothman <joel.noth...@gmail.com> wrote: > Hi Hugo, > > Andrew's approach -- using a list of dicts to specify multiple parameter > grids -- is the correct one. > > However, Andrew, you don't need to include parameters that will be ignored > into your parameter grid. The following will be effectively the same: > > params = > [{'kernel':['poly'],'degree':[1,2,3],'gamma':[1/p,1,2],'coef0':[-1,0,1]}, > {'kernel':['rbf'],'gamma':[1/p,1,2]}, > {'kernel':['sigmoid'],'gamma':[1/p,1,2],'coef0':[-1,0,1]}] > > Joel > > On 27 June 2016 at 20:59, Andrew Howe <ahow...@gmail.com> wrote: > >> 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 >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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