🌐 Mailman 3 May 2018 - scikit-learn - python.org

How does multiple target Ridge Regression work in scikit learn?
by Peer Nowack May 9, 2018

May 9, 2018

Hi all, I am struggling to understand the following: Scikit-learn offers a multiple output version for Ridge Regression, simply by handing over a 2D array [n_samples, n_targets], but how is it implemented? http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge… Is it correct to assume that each regression for each target is independent? Under these circumstances, how can I adapt this to use individual alpha regularization parameters for each regression? If I use GridSeachCV, I would have to hand over a matrix of possible regularization parameters, or how would that work? Thanks in advance - I have been searching for hours but could not find anything on this topic. Peter

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How to pick the maximum possible parameters for algos such as sklearn.decomposition.TruncatedSVD?
by Matti Viljamaa May 9, 2018

May 9, 2018

How to pick the maximum possible parameters for algos such as sklearn.decomposition.TruncatedSVD? Since this algo can cause a memory error, if memory runs out. But of course one would like to select the maximum possible n_components, given the system memory available. So how to do it? Lähetetty Windows 10:n Sähköpostista --- This email has been checked for viruses by Avast antivirus software. https://www.avast.com/antivirus

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Help me Please!
by Santosh Subedi May 8, 2018

May 8, 2018

Hello, I'm using Scikit-learn for Gaussian Process Regression (GPR). I'm facing a problem/confusion regarding GaussianProcessRegressor class. If gp is a GaussianProcessRegressor, the prediction is given as: y_pred_test, sigma = gp.predict(x_test, return_std =True) After printing the y_pred_test and sigma, the y_pred_test predicted for all the data source (3 data source per each test point) at every test point. However, the Standard deviation (sigma) is predicted just a single value at each test point. I want the sigma to be predicted as y_pred_test for every data source. I've asked my query at StackOverflow at the following link: https://stackoverflow.com/questions/50185399/insufficient- output-with-predictx-test-return-std-true-in-gaussianprocessre Could you reply with an appropriate answer to this email or at the StackOverflow, please? Thank you for your time and consideration. Kindly Regards, santobedi

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Does sklearn.decomposition.TruncatedSVD take n_components in order? Or can I select which features I want?
by Matti Viljamaa May 6, 2018

May 6, 2018

Does sklearn.decomposition.TruncatedSVD take n_components in order? Or can I select which features I want? Reason being that if one uses the ”pick features with eigenvalues > 1” principle, then I’d need to tell the SVD algo somehow, which components it should use. BR, Matti Lähetetty Windows 10:n Sähköpostista --- This email has been checked for viruses by Avast antivirus software. https://www.avast.com/antivirus

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Retracting model from the 'blackbox' SVM
by Wouter Verduin May 6, 2018

May 6, 2018

Dear developers of Scikit, I am working on a scientific paper on a predictionmodel predicting complications in major abdominal resections. I have been using scikit to create that model and got good results (score of 0.94). This makes us want to see what the model is like that is made by scikit. As for now we got 100 input variables but logically these arent all as usefull as the others and we want to reduce this number to about 20 and see what the effects on the score are. *My question*: Is there a way to get the underlying formula for the model out of scikit instead of having it as a 'blackbox' in my svm function. At this moment i am predicting a dichtomous variable with 100 variables, (continuous, ordinal and binair). My code: import numpy as npfrom numpy import *import pandas as pdfrom sklearn import tree, svm, linear_model, metrics, preprocessingimport datetimefrom sklearn.model_selection import KFold, cross_val_score, ShuffleSplit, GridSearchCVfrom time import gmtime, strftime #database openen en voorbereiden file = "/home/wouter/scikit/DB_SCIKIT.csv" DB = pd.read_csv(file, sep=";", header=0, decimal= ',').as_matrix() DBT = DBprint "Vorm van de DB: ", DB.shape target = []for i in range(len(DB[:,-1])): target.append(DB[i,-1]) DB = delete(DB,s_[-1],1) #Laatste kolom verwijderenAantalOutcome = target.count(1)print "Aantal outcome:", AantalOutcomeprint "Aantal patienten:", len(target) A = DB b = target print len(DBT) svc=svm.SVC(kernel='linear', cache_size=500, probability=True) indices = np.random.permutation(len(DBT)) rs = ShuffleSplit(n_splits=5, test_size=.15, random_state=None) scores = cross_val_score(svc, A, b, cv=rs) A = ("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))print A X_train = DBT[indices[:-302]] y_train = []for i in range(len(X_train[:,-1])): y_train.append(X_train[i,-1]) X_train = delete(X_train,s_[-1],1) #Laatste kolom verwijderen X_test = DBT[indices[-302:]] y_test = []for i in range(len(X_test[:,-1])): y_test.append(X_test[i,-1]) X_test = delete(X_test,s_[-1],1) #Laatste kolom verwijderen model = svc.fit(X_train,y_train)print model uitkomst = model.score(X_test, y_test)print uitkomst voorspel = model.predict(X_test)print voorspel And output: Vorm van de DB: (2011, 101)Aantal outcome: 128Aantal patienten: 20112011Accuracy: 0.94 (+/- 0.01) SVC(C=1.0, cache_size=500, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear', max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001, verbose=False)0.927152317881[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] Thanks in advance! with kind regards, Wouter Verduin

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Jeff Levesque: neuroscience related datasets
by Jeffrey Levesque May 6, 2018

May 6, 2018

Hi guys, I want to perform some basic data analysis. Anyone have good recommendations where I can obtain free datasets. I was thinking of trying to do something related to neuroscience. But, kaggle doesn't have many datasets for this focus. Thank you, Jeff Levesque https://github.com/jeff1evesque

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Announcing IMPAC: an IMaging-PsychiAtry Challenge, using data-science to predict autism from brain imaging
by Gael Varoquaux May 5, 2018

May 5, 2018

Dear colleagues, It is my pleasure to announce IMPAC: an IMaging-PsychiAtry Challenge, using data-science to predict autism from brain imaging. https://paris-saclay-cds.github.io/autism_challenge/ This is a machine-learning challenge on brain-imaging data to achieve the best prediction of autism spectrum disorder diagnostic status. We are providing the largest cohort so far to learn such predictive biomarkers, with more than 2000 individuals. There is a total of 9000 euros of prices to win for the best prediction. The prediction quality will be measured on a large hidden test set, to ensure fairness. We provide a simple starting kit to serve as a proof of feasibility. We are excited to see what the community will come up with in terms of predictive models and of score. Best, Gaël -- Gael Varoquaux Senior Researcher, INRIA Parietal NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France Phone: ++ 33-1-69-08-79-68 http://gael-varoquaux.info http://twitter.com/GaelVaroquaux

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Multi learn error.
by aijaz qazi May 5, 2018

May 5, 2018

Dear developers of Scikit , I am working on web page categorization with http://scikit.ml/ . *Question*: I am not able to execute MLkNN code on the link http://scikit.ml/api/classify.html. I have installed py 3.6. I found scipy versions not compatible with scikit.ml 0.0.5. Which version of scipy would work with scikit.ml 0.0.5. Kindly let me know. *Regards,* *Aijaz A.Qazi *

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Re: [scikit-learn] Retracting model from the 'blackbox' SVM (Sebastian Raschka)
by David Burns May 4, 2018

May 4, 2018

Hi Sebastian, If you are looking to reduce the feature space for your model, I suggest you look at the scikit-learn page on doing just that http://scikit-learn.org/stable/modules/feature_selection.html David On 2018-05-04 12:00 PM, scikit-learn-request(a)python.org wrote: > Send scikit-learn mailing list submissions to > scikit-learn(a)python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/scikit-learn > or, via email, send a message with subject or body 'help' to > scikit-learn-request(a)python.org > > You can reach the person managing the list at > scikit-learn-owner(a)python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of scikit-learn digest..." > > > Today's Topics: > > 1. Re: Retracting model from the 'blackbox' SVM (Sebastian Raschka) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Fri, 4 May 2018 05:51:26 -0400 > From: Sebastian Raschka <mail(a)sebastianraschka.com> > To: Scikit-learn mailing list <scikit-learn(a)python.org> > Subject: Re: [scikit-learn] Retracting model from the 'blackbox' SVM > Message-ID: > <5331A676-D6C6-4F01-8A4D-EDDE9318E08F(a)sebastianraschka.com> > Content-Type: text/plain; charset=us-ascii > > Dear Wouter, > > for the SVM, scikit-learn wraps the LIBSVM and LIBLINEAR. I think the scikit-learn class SVC uses LIBSVM for every kernel. Since you are using the linear kernel, you could use the more efficient LinearSVC scikit-learn class to get similar results. I guess this in turn is easier to handle in terms of > >> Is there a way to get the underlying formula for the model out of scikit instead of having it as a 'blackbox' in my svm function. > More specifically, LinearSVC uses the _fit_liblinear code available here: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/svm/base.py > > And more info on the LIBLINEAR library it is using can be found here: https://www.csie.ntu.edu.tw/~cjlin/liblinear/ (they have links to technical reports and implementation details there) > > Best, > Sebastian > >> On May 4, 2018, at 5:12 AM, Wouter Verduin <wouterverduin(a)gmail.com> wrote: >> >> Dear developers of Scikit, >> >> I am working on a scientific paper on a predictionmodel predicting complications in major abdominal resections. I have been using scikit to create that model and got good results (score of 0.94). This makes us want to see what the model is like that is made by scikit. >> >> As for now we got 100 input variables but logically these arent all as usefull as the others and we want to reduce this number to about 20 and see what the effects on the score are. >> >> My question: Is there a way to get the underlying formula for the model out of scikit instead of having it as a 'blackbox' in my svm function. >> >> At this moment i am predicting a dichtomous variable with 100 variables, (continuous, ordinal and binair). >> >> My code: >> >> import numpy as >> np >> >> from numpy import * >> import pandas as >> pd >> >> from sklearn import tree, svm, linear_model, metrics, >> preprocessing >> >> import >> datetime >> >> from sklearn.model_selection import KFold, cross_val_score, ShuffleSplit, GridSearchCV >> from time import gmtime, >> strftime >> >> >> #database openen en voorbereiden >> >> file >> = "/home/wouter/scikit/DB_SCIKIT.csv" >> >> DB >> = pd.read_csv(file, sep=";", header=0, decimal= ',').as_matrix() >> >> DBT >> = >> DB >> >> print "Vorm van de DB: ", DB. >> shape >> target >> = [] >> for i in range(len(DB[:,-1])): >> >> target >> .append(DB[i,-1]) >> >> DB >> = delete(DB,s_[-1],1) #Laatste kolom verwijderen >> AantalOutcome = target.count(1) >> print "Aantal outcome:", AantalOutcome >> print "Aantal patienten:", len(target) >> >> >> A >> = >> DB >> b >> = >> target >> >> >> print len(DBT) >> >> >> svc >> =svm.SVC(kernel='linear', cache_size=500, probability=True) >> >> indices >> = np.random.permutation(len(DBT)) >> >> >> rs >> = ShuffleSplit(n_splits=5, test_size=.15, random_state=None) >> >> scores >> = cross_val_score(svc, A, b, cv=rs) >> >> A >> = ("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2)) >> print >> A >> >> X_train >> = DBT[indices[:-302]] >> >> y_train >> = [] >> for i in range(len(X_train[:,-1])): >> >> y_train >> .append(X_train[i,-1]) >> >> X_train >> = delete(X_train,s_[-1],1) #Laatste kolom verwijderen >> >> >> X_test >> = DBT[indices[-302:]] >> >> y_test >> = [] >> for i in range(len(X_test[:,-1])): >> >> y_test >> .append(X_test[i,-1]) >> >> X_test >> = delete(X_test,s_[-1],1) #Laatste kolom verwijderen >> >> >> model >> = svc.fit(X_train,y_train) >> print >> model >> >> uitkomst >> = model.score(X_test, y_test) >> print >> uitkomst >> >> voorspel >> = model.predict(X_test) >> print voorspel >> And output: >> >> Vorm van de DB: (2011, 101) >> Aantal outcome: 128 >> Aantal patienten: 2011 >> 2011 >> Accuracy: 0.94 (+/- 0.01) >> >> SVC >> (C=1.0, cache_size=500, class_weight=None, coef0=0.0, >> >> decision_function_shape >> ='ovr', degree=3, gamma='auto', kernel='linear', >> >> max_iter >> =-1, probability=True, random_state=None, shrinking=True, >> >> tol >> =0.001, verbose=False) >> 0.927152317881 >> [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. >> >> >> 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] >> Thanks in advance! >> >> with kind regards, >> >> Wouter Verduin >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn(a)python.org >> https://mail.python.org/mailman/listinfo/scikit-learn > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > scikit-learn mailing list > scikit-learn(a)python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > > ------------------------------ > > End of scikit-learn Digest, Vol 26, Issue 5 > *******************************************

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Project Contribution
by prince jha May 3, 2018

May 3, 2018

Hi Bill, actually i have used scikit learn for solving problems in which are available in kaggle but i am not so profiecient since i have not used it much. Thanks Prince

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