Mailman 3 October 2016 - scikit-learn

Using logistic regression with count proportions data
by Raphael C Oct. 10, 2016

Oct. 10, 2016

I am trying to perform regression where my dependent variable is constrained to be between 0 and 1. This constraint comes from the fact that it represents a count proportion. That is counts in some category divided by a total count. In the literature it seems that one common way to tackle this is to use logistic regression. However, it appears that in scikit learn logistic regression is only available as a classifier (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Logis… ) . Is that right? Is there another way to perform regression using scikit learn where the dependent variable is a count proportion? Thanks for any help. Raphael

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MLP Classifier error in 0.18 version
by Aakash Agarwal Oct. 7, 2016

Oct. 7, 2016

Hi Guys, I am playing around MLP classifier lately. So i have about 450 inputs to classify. Each input is a vector of array size 50. I am trying to fit the model with 90% as train data. Size of training data: (398, 50) Size of testing data: (45, 50) MLP instantiation: gen_class = MLPClassifier(hidden_layer_sizes=(200,),max_iter=3000,learning_rate='adaptive',alpha=0.025,warm_start=True) Batch size is auto so it is taking 200 as batch_size. But when i am fitting the classifier model, i am getting following error: Traceback (most recent call last): File "intent_detection_classifier_selection.py", line 452, in <module> sk_class.gen_class_fitting(gen_class,corp_lsi_train,train_label) File "intent_detection_classifier_selection.py", line 77, in gen_class_fitting gen_class.fit(data,label) File "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py", line 612, in fit return self._fit(X, y, incremental=False) File "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py", line 372, in _fit intercept_grads, layer_units, incremental) File "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py", line 509, in _fit_stochastic coef_grads, intercept_grads) File "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py", line 225, in _backprop loss = LOSS_FUNCTIONS[self.loss](y, activations[-1]) File "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/_base.py", line 222, in log_loss return -np.sum(y_true * np.log(y_prob)) / y_prob.shape[0] ValueError: operands could not be broadcast together with shapes (200,128) (200,125) Thanks, Aakash

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out of core in ARD or Bayessian Ridge Regression
by Themis Mavridis Oct. 7, 2016

Oct. 7, 2016

I would like to perform out-of-core training using Bayesian Ridge Regression or ARD. Is there any plan to implement such a functionality? Thanks, Themis

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Adding a function that Calculates Weight of Evidence and Information Value
by urvesh patel Oct. 5, 2016

Oct. 5, 2016

> > > I have been using R extensively until last few months when I started using > Python. I noticed that Python doesn't have a function to compute > information value and weight of evidence. Detailed explanation - > http://multithreaded.stitchfix.com/blog/2015/08/13/weight-of-evidence/ > > I have version 0 of this concept ready and I would like to contribute to > scikit-learn so that more and more people can use it. What are the steps I > need to follow in order to do so ? > > -- > Thanking You, > > Urvesh Patel > Data Ninja > Udacity >

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wrapper for GraphCut or GridCut
by Jiří Borovec Oct. 5, 2016

Oct. 5, 2016

Hello, I was thinking about adding GraphCut ( http://www.csd.uwo.ca/~yuri/Papers/pami01.pdf) of GridCut ( http://www.gridcut.com/) which both of them are already implemented in C/C++ a some of then have also wrapper in Python. What is the statement to this task, having GraphCut included in this library such that using thier C/C++ code and include wrappers. Thanks -- Best regards, Jiri Borovec ------------------------------------------------------------------------ Ing. Jiri Borovec, MSc <jiri.borovec(a)fel.cvut.cz> PhD student at CMP CTU, http://cmp.felk.cvut.cz/~borovji3

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Identifying column names of Non-zero values
by Startup Hire Oct. 5, 2016

Oct. 5, 2016

Hi Pypers, Hope you are doing well. I am working on a project to find out the column names of non-zero values at a row level. How can this effectively done in python pandas/dataframe? For example, *Column1* *Column *2 *Column *3 Column 4 Column 5 Column 6 *Column 7* New column to be created 1 1 1 0 0 0 1 Column1,Column 2,Column 3,Column7 I might have to do it on approximately million rows Regards, Sanant

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Random Forest with Bootstrapping
by Ibrahim Dalal Oct. 4, 2016

Oct. 4, 2016

Dear Developers, >From whatever little knowledge I gained last night about Random Forests, each tree is trained with a sub-sample of original dataset (usually with replacement)?. (Note: Please do correct me if I am not making any sense.) RandomForestClassifier has an option of 'bootstrap'. The API states the following > The sub-sample size is always the same as the original input sample size > but the samples are drawn with replacement if bootstrap=True (default). Now, what I am not able to understand is - if entire dataset is used to train each of the trees, then how does the classifier estimates the OOB error? None of the entries of the dataset is an oob for any of the trees. (Pardon me if all this sounds BS) Help this mere mortal. Thanks

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sample_weight for cohen_kappa_score
by Poughon Victor Oct. 4, 2016

Oct. 4, 2016

Hello, I'd like to use samples weights together with sklearn.metrics.cohen_kappa_score, in a similar way to other metrics which have this argument. Is it as simple as forwarding the weights to the confusion_matrix call? [0] If yes I'm happy to work on the pull request. In that case the other argument "weights" might be confusing but it's too late to rename it, right? Cheers, Victor Poughon [0] https://github.com/scikit-learn/scikit-learn/blob/dee786a/sklearn/metrics/c…

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Generate data from trained naive bayes
by klo uo Oct. 3, 2016

Oct. 3, 2016

Hi, because naive bayes is a generative model, does that mean that I can somehow generate data based on trained model? For example: clf = BernoulliNB() clf.fit(train, labels) Can I generate data for specific label? Thanks, Klo

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suggested machine learning algorithm
by Thomas Evangelidis Oct. 2, 2016

Oct. 2, 2016

Dear scikit-learn users and developers, I have a dataset consisting of 42 observation (molnames) and 4 variables ( VDWAALS, EEL, EGB, ESURF) with which I want to make a predictive model that estimates the experimental value (Expr). I tried multivariate linear regression using 10,000 bootstrap repeats each time using 21 observations for training and the rest 21 for testing, but the average correlation was only R= 0.1727 +- 0.19779. molname VDWAALS EEL EGB > ESURF Expr > CHEMBL108457 -20.4848 -96.5826 23.4584 -5.4045 > -7.27193 > CHEMBL388269 -50.3860 28.9403 -51.5147 -6.4061 > -6.8022 > CHEMBL244078 -49.1466 -21.9869 17.7999 -6.4588 > -6.61742 > CHEMBL244077 -53.4365 -32.8943 34.8723 -7.0384 > -6.61742 > CHEMBL396772 -51.4111 -34.4904 36.0326 -6.5443 > -5.82207 > ........ I would like your advice about what other machine learning algorithm I could try with these data. E.g. can I make a decision tree or the observations and variable are too few to avoid overfitting? I could include more variables but the observations will always remain 42. I would greatly appreciate any advice! Thomas

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