🌐 Mailman 3 July 2017 - scikit-learn - python.org

Re: [scikit-learn] Nearest neighbor search with 2 distance measures
by "Умнов Алексей (Alexey Umnov)" Aug. 2, 2017

Aug. 2, 2017

Здравствуйте! Я в данный момент в отпуске, и вернусь 15 августа. -- Умнов Алексей

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Re: [scikit-learn] Nearest neighbor search with 2 distance measures
by "Умнов Алексей (Alexey Umnov)" July 30, 2017

July 30, 2017

Здравствуйте! Я в данный момент в отпуске, и вернусь 15 августа. -- Умнов Алексей

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Nearest neighbor search with 2 distance measures
by Rohin Kumar July 30, 2017

July 30, 2017

Dear all, This is my first post on this forum. May be it is a feature request or about something I don't know how to get it work. My question is on BallTree algorithm with custom metrics. I am working with a dataset for which I was calculating two point correlation with one distance metric using BallTree algorithm. Say import numpy as npfrom sklearn.neighbors import BallTree np.random.seed(0) X = np.random.random((30, 3)) r = np.linspace(0, 1, 5) tree = BallTree(X,metric='euclidean') tree.two_point_correlation(X, r) Now, I want to calculate two-point correlation based on two different metrics. Imagine I want to find correlation based on their distances on XZ and YZ planes - group neighbors based on two distances instead of one. Say I want to find correlation within r1 and r2 bins based on two different distance metrics say something like r1 = np.linspace(0, 1, 5) r2 = np.linspace(0, 1, 5) tree = BallTree(X,metric1=’euclidean2D’,metric2=’euclidean2D’) tree.two_point_correlation(X, r1, r2) How can I go about doing that? Goal is to get a contour plot of two-point correlation with r1 and r2 as axes. Any help on this would be great! Thanks in advance, Rohin.

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Are sample weights normalized?
by Abhishek Raj July 28, 2017

July 28, 2017

Hi, I am using one class svm for binary classification and was just curious what is the range/scale for sample weights? Are they normalized internally? For example - Sample 1, weight - 1 Sample 2, weight - 10 Sample 3, weight - 100 Does this mean Sample 3 will always be predicted as positive and sample 1 will never be predicted as positive? What about sample 2? Also, what would happen if I assign a high weight to majority of the samples and low weights to the rest. Eg if 80% of my samples were weighted 1000 and 20% were weighted 1. A clarification or a link to read up on how exactly weights affect the training process would be really helpful. Thanks, Abhishek

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Question about the Library of “sklearn.neural_network.BernoulliRBM” that Creates Highly Correlated Features.
by Masanari Kondo July 27, 2017

July 27, 2017

Dear all, I’m using the sklearn library to generate new features of a dataset using a Restricted Boltzmann Machine (RBM, sklearn.neural_network.BernoulliRBM). I use the following environment: python 3.5.0 numpy==1.11.1 scikit-learn==0.18 I have already tried a large number of iterations (n_iter=6000) and a low learning rate (0.0001) for all training data (373 samples). However, The new features that are generated by the RBM are all highly correlated. Can anyone explain why this happens? Below is a MWE: import numpy as np import csv from sklearn.neural_network import BernoulliRBM # train data train_data = np.array( [[0.0326086956522,0.0,0.0,0.0200400801603,0.0674157303371,0.000805152979066,0.00200803212851,0.243243243243,0.0123456790123,0.55,0.0233428760185,0.0,0.0,0.0,0.444444444,0.0,0.0,0.157556270138,0.0188679245283,0.0983652512615], [0.0108695652174,0.2,0.0,0.00200400801603,0.0112359550562,0.0,0.0,0.027027027027,0.0123456790123,1.0,0.00154151068047,0.0,0.0,1.0,1.0,0.0,0.0,0.0289389067571,0.0,0.0], [0.0869565217391,0.0,0.152542372881,0.0260521042084,0.0749063670412,0.00322061191626,0.0180722891566,0.108108108108,0.0987654320988,0.4,0.022241796961,0.2,0.0909090909091,0.0,0.40625,0.0,0.0,0.053054662388,0.0188679245283,0.129097937384], [0.0326086956522,0.2,0.0847457627119,0.0140280561122,0.0149812734082,0.000268384326355,0.0120481927711,0.027027027027,0.0246913580247,0.25,0.00352345298392,1.0,0.0,0.75,0.555555556,0.0,0.0,0.0192926045047,0.0188679245283,0.0983652512615], [0.0978260869565,0.0,0.0,0.0100200400802,0.0711610486891,0.00214707461084,0.00803212851406,0.027027027027,0.111111111111,0.265625,0.0262056815679,1.0,0.0,0.0,0.518518519,0.0,0.0,0.0568060021635,0.0566037735849,0.213107498008], [0.0760869565217,0.8,0.0,0.0180360721443,0.0936329588015,0.0,0.0120481927711,0.0810810810811,0.0864197530864,0.3333333335,0.0561550319313,0.0,0.0,0.863636364,0.342857143,0.5,0.333333333333,0.168121267841,0.169811320755,0.463705037033], [0.0978260869565,1.0,0.0,0.0100200400802,0.063670411985,0.00697799248524,0.0,0.135135135135,0.0740740740741,0.4166666665,0.0156353226162,0.0,0.0,0.949367089,0.333333333,0.25,0.266666666667,0.0316184351626,0.0566037735849,0.163932249402], [0.0326086956522,0.2,0.0,0.0380761523046,0.0374531835206,0.000805152979066,0.0281124497992,0.135135135135,0.037037037037,1.0,0.00836820083682,0.0,0.0,0.923076923,0.583333333,0.0,0.0,0.0562700964881,0.0188679245283,0.0491752486057], [0.0108695652174,0.0,0.0,0.0200400801603,0.00374531835206,0.0,0.0160642570281,0.0540540540541,0.0123456790123,1.0,0.000220215811495,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0188679245283,0.147540499867], [0.217391304348,0.0,0.0,0.0140280561122,0.295880149813,0.0365002683843,0.0100401606426,0.135135135135,0.123456790123,0.4487534625,0.183880202599,1.0,0.0909090909091,0.0,0.19375,0.0,0.0,0.191961414822,0.188679245283,0.287703974741], [0.0652173913043,0.0,0.0,0.0160320641283,0.0224719101124,0.00402576489533,0.0140562248996,0.027027027027,0.0740740740741,1.0,0.00132129486897,0.0,0.0,0.0,0.444444444,0.0,0.0,0.0,0.0188679245283,0.147540499867], [0.0326086956522,0.6,0.0,0.0100200400802,0.0411985018727,0.000268384326355,0.00200803212851,0.108108108108,0.0123456790123,0.25,0.00902884827131,1.0,0.0909090909091,0.971428571,0.75,0.25,0.133333333333,0.0594855305401,0.0566037735849,0.147540499867], [0.119565217391,0.2,0.0,0.0140280561122,0.0973782771536,0.0,0.0100401606426,0.0540540540541,0.135802469136,0.29,0.0398590618806,1.0,0.0,0.529411765,0.409090909,0.0,0.0,0.0723472668927,0.0188679245283,0.107306205553], [0.0326086956522,0.2,0.0,0.0100200400802,0.0262172284644,0.000268384326355,0.00200803212851,0.108108108108,0.037037037037,0.25,0.00638625853336,1.0,0.0,0.818181818,0.666666667,0.0,0.0,0.0401929260499,0.0188679245283,0.0983652512615], [0.173913043478,0.4,0.0,0.0300601202405,0.243445692884,0.020397208803,0.0,0.405405405405,0.16049382716,0.46,0.106364236952,1.0,0.0,0.725490196,0.311111111,0.0,0.0,0.136254019315,0.169811320755,0.230532031043], [0.163043478261,0.4,0.0,0.0180360721443,0.153558052434,0.0,0.0,0.243243243243,0.185185185185,0.3392857145,0.044924025545,1.0,0.0909090909091,0.725490196,0.225,0.25,0.133333333333,0.0594855305401,0.0377358490566,0.226223848446], [0.152173913043,0.6,0.0508474576271,0.0220440881764,0.10861423221,0.0228126677402,0.00602409638554,0.216216216216,0.135802469136,0.2884615385,0.0237833076415,1.0,0.0909090909091,0.759259259,0.321428571,0.0,0.0,0.0316949931128,0.0754716981132,0.189692820679], [0.29347826087,0.4,0.0,0.0160320641283,0.378277153558,0.0421363392378,0.0100401606426,0.0810810810811,0.185185185185,0.4123931625,0.283197533583,0.888888889,0.0909090909091,0.294117647,0.183760684,0.25,0.466666666667,0.220078599537,0.0754716981132,0.163932249402], [0.0326086956522,0.0,0.0,0.00400801603206,0.0112359550562,0.000805152979066,0.00401606425703,0.0,0.037037037037,0.75,0.000880863245981,0.0,0.0,0.0,0.666666667,0.0,0.0,0.0,0.0188679245283,0.147540499867], [0.597826086957,0.4,0.135593220339,0.0400801603206,0.397003745318,0.352388620505,0.0160642570281,0.324324324324,0.111111111111,0.4782763535,0.249504514424,1.0,0.181818181818,0.406593407,0.195454545,0.0,0.0,0.0922537270084,0.188679245283,0.273613857004]] ) # define the RBM model random_state = 200 model = BernoulliRBM(n_components=10,n_iter=10,random_state=random_state) # building RBM and creating RBM features # Each column means one feature, each row means one line of the train data. RBM_feature_data = model.fit_transform(train_data) print(RBM_feature_data) Thank you! Masanari Kondo

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Maximum Dissimilarity Sampling
by Shyam Saladi July 27, 2017

July 27, 2017

Hello all, I'm looking to sample a large dataset for a subset that best covers the space. One way of doing this would be maximum dissimilarity, say as implemented in R as part of caret::maxDissim <https://rdrr.io/cran/caret/man/maxDissim.html>. Is anyone are of similar functionality available as part of a common Python package, perhaps in scikit-learn? Many thanks in advance, Shyam

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Fwd: Custom transformer failing check_estimator test
by Sam Barnett July 26, 2017

July 26, 2017

Dear scikit-learn developers, I am developing a transformer, named Sqizer, that has the ultimate goal of modifying a kernel for use with the sklearn.svm package. When given an input data array X, Sqizer.transform(X) should have as its output the Gram matrix for X using the modified version of the kernel. Here is the code for the class so far: class Sqizer(BaseEstimator, TransformerMixin): def __init__(self, C=1.0, kernel='rbf', degree=3, gamma=1, coef0=0.0, cut_ord_pair=(2,1)): self.C = C self.kernel = kernel self.degree = degree self.gamma = gamma self.coef0 = coef0 self.cut_ord_pair = cut_ord_pair def fit(self, X, y=None): # Check that X and y have correct shape X, y = check_X_y(X, y) # Store the classes seen during fit self.classes_ = unique_labels(y) self.X_ = X self.y_ = y return self def transform(self, X): X = check_array(X, warn_on_dtype=True) """Returns Gram matrix corresponding to X, once sqized.""" def kPolynom(x,y): return (self.coef0+self.gamma*np.inner(x,y))**self.degree def kGauss(x,y): return np.exp(-self.gamma*np.sum(np.square(x-y))) def kLinear(x,y): return np.inner(x,y) def kSigmoid(x,y): return np.tanh(self.gamma*np.inner(x,y) +self.coef0) def kernselect(kername): switcher = { 'linear': kPolynom, 'rbf': kGauss, 'sigmoid': kLinear, 'poly': kSigmoid, } return switcher.get(kername, "nothing") cut_off = self.cut_ord_pair[0] order = self.cut_ord_pair[1] from SeqKernel import hiSeqKernEval def getGram(Y): gram_matrix = np.zeros((Y.shape[0], Y.shape[0])) for row1ind in range(Y.shape[0]): for row2ind in range(X.shape[0]): gram_matrix[row1ind,row2ind] = \ hiSeqKernEval(Y[row1ind],Y[row2ind],kernselect(self.kernel),\ cut_off,order) return gram_matrix return getGram(X) However, when I run the check_estimator method on Sqizer, I get an error with the following check: # raises error on malformed input for transformif hasattr(X, 'T'): # If it's not an array, it does not have a 'T' property assert_raises(ValueError, transformer.transform, X.T) How do I alter my code to pass this test? Could my estimator trip up on any further tests? I have attached the relevant .py files if you require a bigger picture. This particular snippet comes from the OptimalKernel.py file. Many thanks, Sam Barnett

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Showing loss value in MLPClassifier
by Renato De Leone July 26, 2017

July 26, 2017

Is it possible to show additional information such as current value of the loss function etc in MLPClassifier. Apparently verbose=TRue does not make any difference. Thanks -- Renato

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Classifiers for dataset with categorical features
by Raga Markely July 26, 2017

July 26, 2017

Hello, I am wondering if there are some classifiers that perform better for datasets with categorical features (converted into sparse input matrix with pd.get_dummies())? The data for the categorical features are nominal (order doesn't matter, e.g. country, occupation, etc). If you could provide me some references (papers, books, website, etc), that would be great. Thank you very much! Raga

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scikit-learn hits 20k github stars
by Jacob Schreiber July 22, 2017

July 22, 2017

[image: Inline image 1] Many thanks to everyone who has worked on and contributed to the project for the past decade to make it such a great tool! Also a special thanks to Joel Nothman, who has been on top of answering issues and reviewing PRs for years now. 🎆🎉

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