Mailman 3 August 2016 - scikit-learn

GradientBoostingRegressor, question about initialisation with MeanEstimator
by Алексей Драль Aug. 28, 2016

Aug. 28, 2016

Hi there, I recently found out that GradientBoostingRegressor uses MeanEstimator for the initial estimator in ensemble. Could you please point out (or explain) to the research showing superiority of this approach compared to the usage of DecisionTreeRegressor? -- Yours sincerely, Alexey A. Dral

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Fitting a plane to a 3D points Cloud
by Siddhant Loya Aug. 26, 2016

Aug. 26, 2016

I have been trying to use Ransac to fit a plane to a 3D point cloud. I am not able to understand on how to do this on 3D data. I have already posted a question on SO. Link :- http://stackoverflow.com/questions/39159102/fit-a-plane-to-3d-point-cloud-u… I am not able to understand how to solve this for a plane instead of 2-D line. Regards, Siddhant

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Re: [scikit-learn] Building Scikit Learn in Win 7 64bits
by KevNo Aug. 23, 2016

Aug. 23, 2016

Hello, Thanks for yoru advice/reply. I tried to build from VC after following Instructions of VS Python 2.7 compiler. Steps ares: |1) Git download 2) VC++ for Python: https://blogs.msdn.microsoft.com/pythonengineering/2016/04/11/unable-to-fin… 3) Change thePath for compiler VStudio 4) in|!!cd D:\_devs\Python01\scikit_learn\sklearn (folder of sklearn) |5) python setup.py build | I have this message: |building'sklearn.__check_build._check_build' extension compiling C sources cl.exe/c/nologo/Ox /MD/W3/GS- /DNDEBUG-ID:\_devs\Python01\Anaconda2\lib\si te-packages\numpy\core\include-ID:\_devs\Python01\Anaconda2\lib\site-packages\n umpy\core\include-ID:\_devs\Python01\Anaconda2\include-ID:\_devs\Python01\Anac onda2\PC/Tc_check_build.c/Fobuild\temp.win-amd64-2.7\Release\_check_build.obj Found executable C:\Users\asus1\AppData\Local\Programs\Common\Microsoft\Visual C ++ for Python\9.0\VC\Bin\amd64\cl.exe _check_build.c c1: fatal error C1083: Cannot open source file: '_check_build.c': No such file or directory| I dont have any idea where it could come from Thanks Brook > scikit-learn-request(a)python.org <mailto:scikit-learn-request@python.org> > Tuesday, August 23, 2016 1:00 AM > 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: Building Scikit Learn in Win 7 64bits (Olivier Grisel) > 2. Re: Building Scikit Learn in Win 7 64bits (Joel Nothman) > 3. Re: 0.18? (Olivier Grisel) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Mon, 22 Aug 2016 11:43:21 +0200 > From: Olivier Grisel <olivier.grisel(a)ensta.org> > To: Scikit-learn user and developer mailing list > <scikit-learn(a)python.org> > Subject: Re: [scikit-learn] Building Scikit Learn in Win 7 64bits > Message-ID: > <CAFvE7K6SdMjQ8LM9-RamxhKZNx0gsemoAGVUzr8tYAmSMGgjgw(a)mail.gmail.com> > Content-Type: text/plain; charset=UTF-8 > > The error message mentions gcc. Have you installed some mingw version? > > As of now our windows build is only properly tested with the Visual > Studio C++ compiler from appveyor: > > https://ci.appveyor.com/project/sklearn-ci/scikit-learn > > I have not tested the build with mingwpy in a while (I am not a > windows user my-self). > > The file not found error makes me think that you might need to cd into > the scikit-learn source folder: > > !!cd D:\_devs\Python01\scikit_learn\sklearn > !!python setup.py build_ext --inplace >

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Help with improving t-sne
by Shanglun Wang Aug. 22, 2016

Aug. 22, 2016

Hello, I am currently working on a ticket on github involving improving the data structures powering t-sne. I am running into some trouble trying to conceptually link up what the code is doing and the underlying mathematical theory. Normally I would just grapple with it, but I feel like I would need some help to get this ticket done in a reasonable time frame. Would someone be willing to help me understand the theory underpinning t-sne, and how that links up with the implementation? Thank you, Sean

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0.18?
by Joel Nothman Aug. 22, 2016

Aug. 22, 2016

Has there been talk about a release? We've long-since merged the big changes to CV. Among the things seeming unfinished there is that where `cv` is available, `fit` should also support a `labels` parameter. That's not available in RFECV etc. There are some other nice features in the next release too, and I think we should move towards putting them out there. Should we start tagging issues in and out of the 0.18 milestone?

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Building Scikit Learn in Win 7 64bits
by KevNo Aug. 22, 2016

Aug. 22, 2016

Hi, I follow the instructions here: to compile Scikit-learn for development https://github.com/scikit-learn/scikit-learn/issues/5709 1) Download from Git into a repository scikit_learn 2) Add the path to Python Path 3) In IPython !!python D:\_devs\Python01\scikit_learn\sklearn\setup.py build_ext --inplace and I got this message: |Assuming default configuration (scikit_learn\\sklearn\\svm\\tests/{setup_tests,setup}.py was not found)D:\\\\_devs\\\\Python01\\\\scikit_learn\\\\sklearn\\\\setup.py:71: UserWarning: ', ' Blas (http://www.netlib.org/blas/) libraries not found.', ' Directories to search for the libraries can be specified in the', ' numpy/distutils/site.cfg file (section [blas]) or by setting', ' the BLAS environment variable.', ' warnings.warn(BlasNotFoundError.__doc__)', 'Warning: Assuming default configuration (scikit_learn\\sklearn\\linear_model\\tests/{setup_tests,setup}.py was not found)Warning: Assuming default configuration (scikit_learn\\sklearn\\utils\\sparsetools\\tests/{setup_tests,setup}.py was not found)Warning: Assuming default configuration (scikit_learn\\sklearn\\utils\\tests/{setup_tests,setup}.py was not found)Warning: Assuming default configuration (scikit_learn\\sklearn\\tests/{setup_tests,setup}.py was not found)gcc.exe: error: _check_build.c: No such file or directory', 'gcc.exe: fatal error: no input files', 'compilation terminated.', 'error: Command "gcc -m64 -g -DNDEBUG -DMS_WIN64 -O2 -Wall -Wstrict-prototypes -D__MSVCRT_VERSION__=0x0900 -ID:\\_devs\\Python01\\WinPython-64-2710\\python-2.7.10.amd64\\lib\\site-packages\\numpy\\core\\include -ID:\\_devs\\Python01\\WinPython-64-2710\\python-2.7.10.amd64\\lib\\site-packages\\numpy\\core\\include -ID:\\_devs\\Python01\\WinPython-64-2710\\python-2.7.10.amd64\\include -ID:\\_devs\\Python01\\WinPython-64-2710\\python-2.7.10.amd64\\PC -c _check_build.c -o build\\temp.win-amd64-2.7\\Release\\_check_build.o" failed with exit status 1']| Environnment WinPython 2.7 64bits Windows-7-6.1.7601-SP1 ('Python', '2.7.10 (default, May 23 2015, 09:44:00) [MSC v.1500 64 bit (AMD64)]') ('NumPy', '1.9.2') ('SciPy', '0.16.0' ) Just wondering if possible to get a place where we can compile without spending hours and days on the web to find the issues ? Thanks Brook

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update pydata schedule
by Sebastian Raschka Aug. 18, 2016

Aug. 18, 2016

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Hiring: Cloud and HPC Engineer at NIMH in Bethesda, MD
by Thomas, Adam (NIH/NIMH) [E] Aug. 17, 2016

Aug. 17, 2016

HIRING: CLOUD AND HPC ENGINEER The National Institute of Mental Health (NIMH) is the lead federal agency for research on mental disorders. NIMH is one of the 27 Institutes and Centers that make up the National Institutes of Health (NIH), which is responsible for all federally funded biomedical research in US. NIH is part of the U.S. Department of Health and Human Services (HHS). The NIH is a highly rated employer at glassdoor.com with very competitive salary and benefits packages. The Data Science and Sharing Team (DSST) is a new group created to develop and support data sharing and other data-intensive scientific projects within the NIMH Intramural Research Program (IRP). Working closely with the Office of Data Science the goal of the DSST is to make the NIMH IRP a leader in the open science and data sharing practices mandated by the Open Data Policy released by the White House on 9 May, 2013. We are building a team to make that happen. What you’ll do… --------------- BUILD You will work with a team of researchers and developers to build and deploy neuroimaging data processing pipelines for investigators within the NIMH IRP. You will collaborate with and contribute to other projects throughout the world that are building standards and tools for open and reproducible neuroscience (e.g., NiPy, BIDS, Binder, Rstudio). You'll have the resources of the NIH HPC Cluster at your disposal as well as additional help from the AWS cloud. All tools and code will be open source and freely distributed. TEACH You will work to bolster data science skills within the NIMH IRP by teaching courses to scientists on best data practices (e.g. Software & Data Carpentry) as well as accessing and using specific neuroimaging repositories (e.g. The Human Connectome Project, OpenfMRI, UK Biobank). QUANTIFY There is no use building tools for open science if no one uses them. Part of the job of the DSST is to measure data sharing and open science practices within the NIMH IRP and progress toward their adoption. This will include bibliometrics for scientific publications from the NIMH IRP and other measures of data sharing and secondary data utilization. You will provide crucial systems level support to the team in gauging this progress. Who you are… ------------ EXPERIENCED You should be very comfortable on the command line and have a rock-solid handle on one or more Unix-based operating systems. You should have some experience with distributed, high-performance computing tools such as Spark, OpenStack, Docker/Singularity, and batch processing systems such as SLURM and SGE. You should also have experience coding in modern languages currently used in data-intensive, scientific computing such as Python, R, and Javascript, as well as interfacing with a variety of APIs. PROVEN Ideally we would like to see a recent degree (BS, MS, or PhD) in a STEM field, but if you can prove you have an equivalent amount of expertise with your publications, projects, or github/kaggle ranking, we’re all ears. We are also interviewing students and part-time staff if you’re still working on your degree. DRIVEN Data science is moving fast – we’re looking for someone who can move faster. You should be a self-learner and a self-starter. Provide some examples of things you have worked on independently. How to apply… ------------- Email your resume, a cover letter, and a code sample that demonstrates you are all three of the above to: DATASCI-JOBSEARCH(a)mail.nih.gov The National Institutes of Health is an equal opportunity employer.

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Inconsistent Logistic Regression fit results
by Chris Cameron Aug. 17, 2016

Aug. 17, 2016

Hi all, Using the same X and y values sklearn.linear_model.LogisticRegression.fit() is providing me with inconsistent results. The documentation for sklearn.linear_model.LogisticRegression states that "It is thus not uncommon, to have slightly different results for the same input data.” I am experiencing this, however the fix of using a smaller “tol” parameter isn’t providing me with consistent fit. The code I’m using: def log_run(logreg_x, logreg_y): logreg_x['pass_fail'] = logreg_y df_train, df_test = train_test_split(logreg_x, random_state=0) y_train = df_train.pass_fail.as_matrix() y_test = df_test.pass_fail.as_matrix() del(df_train['pass_fail']) del(df_test['pass_fail']) log_reg_fit = LogisticRegression(class_weight='balanced',tol=0.000000001).fit(df_train, y_train) predicted = log_reg_fit.predict(df_test) accuracy = accuracy_score(y_test, predicted) kappa = cohen_kappa_score(y_test, predicted) return [kappa, accuracy] I’ve gone out of my way to be sure the test and train data is the same for each run, so I don’t think there should be random shuffling going on. Example output: --- log_run(df_save, y) Out[32]: [0.027777777777777728, 0.53333333333333333] log_run(df_save, y) Out[33]: [0.027777777777777728, 0.53333333333333333] log_run(df_save, y) Out[34]: [0.11347517730496456, 0.58333333333333337] log_run(df_save, y) Out[35]: [0.042553191489361743, 0.55000000000000004] log_run(df_save, y) Out[36]: [-0.07407407407407407, 0.51666666666666672] log_run(df_save, y) Out[37]: [0.042553191489361743, 0.55000000000000004] A little information on the problem DataFrame: --- len(df_save) Out[40]: 240 len(df_save.columns) Out[41]: 18 If I omit this particular column the Kappa no longer fluctuates: df_save[‘abc'].head() Out[42]: 0 0.026316 1 0.333333 2 0.015152 3 0.010526 4 0.125000 Name: abc, dtype: float64 Does anyone have ideas on how I can figure this out? Is there some randomness/shuffling still going on I missed? Thanks! Chris

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Compiled trees
by Ali Zude Aug. 12, 2016

Aug. 12, 2016

Dear All, I am trying to speed up the prediction of Random Forests. I've used compiledtress, which was useful, but since I have 6 models and once I've loaded all of them I got "Multiprocessing exception:" here is my models in the code: ...model1=joblib.load('/models/model1.pkl'') model2=joblib.load('/models/model2.pkl') model3=joblib.load('/models/model3.pkl') model4=compiledtrees.CompiledRegressionPredictor(joblib.load('/models/model4.pkl')) model5=compiledtrees.CompiledRegressionPredictor(joblib.load('/models/model4.pkl')) model6=compiledtrees.CompiledRegressionPredictor(joblib.load('/models/model4.pkl')) model1=compiledtrees.CompiledRegressionPredictor(model1) model2=compiledtrees.CompiledRegressionPredictor(model2) model3=compiledtrees.CompiledRegressionPredictor(model3).... Now I'm trying to use MultiOutputRegressor(RandomForestRegressor()), however, I could not find any tool to do model selection, can anyone help me either to solve the first problem or the second one Best regards

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