Mailman 3 January 2017 - scikit-learn

Generalized Discriminant Analysis with Kernel
by Raga Markely Jan. 10, 2017

Jan. 10, 2017

Hello, I wonder if scikit-learn has implementation for generalized discriminant analysis using kernel approach? http://www.kernel-machines.org/papers/upload_21840_GDA.pdf I did some search, but couldn't find. Thank you, Raga

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Complex variables in Gaussian mixture models?
by Rory Smith Jan. 10, 2017

Jan. 10, 2017

Hi All, I’d like to set up a GMM using mixture.BayesianGaussianMixture to model a probability density of complex random variables (the learned means and covariances should also be complex valued). I wasn’t able to see any mention of how to handle complex variables in the documentation so I’m curious if it’s possible in the current implementation. I tried the obvious thing of first generating a 1D array of complex random numbers, but I see these warning when I try and fit the array X using dpgmm = mixture.BayesianGaussianMixture(n_components=4, covariance_type='full', n_init=1).fit(X) ~/miniconda2/lib/python2.7/site-packages/sklearn/utils/validation.py:382: ComplexWarning: Casting complex values to real discards the imaginary part array = np.array(array, dtype=dtype, order=order, copy=copy) And as might be expected from the warning, the learned means are real. Any advice on this problem would be greatly appreciated! Best, Rory

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Jeff Levesque: Sample SVM / SVR dataset
by Jeffrey Levesque Jan. 8, 2017

Jan. 8, 2017

Hey guys, Im working on developing a web-interface, and programmatic api, to scikit-learn: - https://github.com/jeff1evesque/machine-learning However, I've only interfaced the SVM, and SVR classes. To be thorough, for development within git, I've created unit tests for the Travis CI. But, I made up some bogus datasets, in order to unit test the SVM, and SVR predictions: - dataset: https://github.com/jeff1evesque/machine-learning/tree/master/interface/stat… - unit tests: https://github.com/jeff1evesque/machine-learning/tree/master/test/live_serv… But, I'd prefer to have real data, so the computed prediction is more meaningful, instead of predicating on made up data. The corresponding unit tests I have, simply check if a prediction can be made for the supplied dataset. However, I'd like to check the prediction against a known, expected result, which is the motivation of having real meaningful dataset(s): - https://github.com/jeff1evesque/machine-learning/issues/2751 Does anyone have sample dataset(s) they have used for SVM, or SVR predictions? I'd like my unit tests to be somewhat interesting, yet more meaningful. Thank you, Jeff Levesque https://github.com/jeff1evesque

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modifying CV score
by Jonathan Taylor Jan. 5, 2017

Jan. 5, 2017

(Think this is right reply to from a digest... If not, apologies) Thanks for the pointers. From what I read on the API, I gather that for an estimator with a score method, inside GridSearchCV there will be pseudo-code like ... estimator.fit(X_train, y_train) scorer = estimator.score return scorer(X_test, y_test) For custom scores that are not methods of an estimator, I guess the `make_scorer` function returns a callable with the same signature as a score method of an estimator? -- Jonathan Taylor Dept. of Statistics Sequoia Hall, 137 390 Serra Mall Stanford, CA 94305 Tel: 650.723.9230 Fax: 650.725.8977 Web: http://www-stat.stanford.edu/~jtaylo

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modifying CV score
by Jonathan Taylor Jan. 4, 2017

Jan. 4, 2017

I'm looking for a simple way to get a small pipeline for choosing a parameter using a modification of CV for regression type problems. The modification is pretty simple, so, for squared-error or logistic deviance, it is a simple modification of the score of `Y` (binary labels) and `X.dot(beta)` (linear predictor). I've been trying to understand how to use sklearn for this as there is no need for me to rewrite the basic CV functions. I'd like to be able to use my own custom estimator (so I guess I just need a subclass of BaseEstimator with a `fit` method with (X,y) signature?), as well as my own modification of the score. I'd be happy to understand the code for an estimator whose fit returns `np.zeros(X.shape[1])` and a given scoring function like def score(estimator, X_test, y_test): beta = estimator.parameters_ # which is just a zero vector for my estimator -- I guess this is the way I should extract the linear # predictor linpred = X_test.dot(beta) #or maybe? linpred = estimator.transform(X_test) return np.linalg.norm(y_test - linpred) This would not be an interesting model, but it would help me understand how things are evaluated in the CV loop. I have read how to create a custom scorer in the docs but it does not seem to describe what `estimator` will be inside the CV loop. I presume a custom scorer will get called with values X_test and y_test and I suppose estimator will be a model fit to X_train and y_train? -- Jonathan Taylor Dept. of Statistics Sequoia Hall, 137 390 Serra Mall Stanford, CA 94305 Tel: 650.723.9230 Fax: 650.725.8977 Web: http://www-stat.stanford.edu/~jtaylo

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KNeighborsClassifier and metric='precomputed'
by Pedro Pazzini Jan. 3, 2017

Jan. 3, 2017

Hi all! I'm trying to use a KNeighborsClassifier with precomputed metric. In it's predict method (http://scikit-learn.org/stable/modules/generated/sklearn .neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier.predict) it says the input should be: "(n_query, n_indexed) if metric == ‘precomputed’" What is n_indexed? Shouldn't the shape of the input in the predict method be (n_query,n_query)? How can I use the predict method after fitting the classifier with a distance matrix? Regards, Pedro Pazzini

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