After REP installation from here, I've met the following problem with TMVAClassifier fitting: I'm trying to train TMVAClassifier, and IOError raises after following strings: " baseline = TMVAClassifier(method='kBDT', features=variables, BoostType='Grad', NTrees=40, Shrinkage=0.01, MaxDepth=7, UseNvars=6, nCuts=-1) features=variables)

baseline.fit(train, train['signal'])"

Stacktrace is next: IOError Traceback (most recent call last) in () 3 UseNvars=6, nCuts=-1) 4 # baseline = TMVAClassifier(method='kBDT', NTrees=50, Shrinkage=0.05, features=variables) ----> 5 baseline.fit(train, train['signal'])

IOError: [Errno 2] No such file or directory: '/home/artem/Documents/IPython Notebooks/CERN + Yandex/Original Baseline/flavours-of-physics-start/tmp0Fhtqe/weights/TMVAEstimation_REP_Estimator.weights.xml'

As I found, weights/ folder was created outside of temporary folder instead created inside in last one. It causes the error above.

ROOT 5.34, Python 2.7, GCC 4.8, Ubuntu 14.04 LTS (x64). All requirenments for REP were installed successfully (from requirenments.txt)

Hey,

first of all a compliment: I really like your repo and I build a lot of code on it, it's so useful! About the FoldingClassifier: There was already a request to implement the StratifiedKFolding additionally to the "normal" KFolding. I would be very glad to see this but I'd even go a step further: why don't you completely replace the KFold with a StratifiedKFold?

I think, from an ML point of view, it is always better (or, in best case, equally good) to use a stratified one. Using a normal KFolding only introduces different class-balances which (usually) result in "shifted" probabilities among the different classifier, whereas a stratified one does not and therefore makes each trained classifiers predictions "comparable".

Or in other words: I cannot think of any case where you want to have a non-stratified KFolding instead of a stratified one.

What do you think?

Best, Mayou

I see that some of the continuous integration scripts support conda builds, although not all the dependencies are installed this way. Is there any hope of seeing a build on conda soon for Linux x86_64 systems?

The reason I ask is that I have accounts on numerous batch systems, none of which I have root access or have any way to use docker. They're all linux-based though, as is the norm. So far as I know, this is the case for many researchers.

It'd be great to see a way to quickly install REP on these systems. This would:

• Cut down on the time needed to introduce people to REP
• Hook into the environment management and environment logging provided by conda
• Easily and quickly deploy REP on supercomputing nodes while requiring little of their filesystem

This is especially useful for ensuring the ROOT install is sane. I know there has already been a lot of work in the direction of making REP easy to access and install. Perhaps this could be a healthy addition?

If you would like to run rep with eg a StratifiedKFold instead of a normal KFold, this will be possible after the pull request. If no external folder-object is parsed, the default KFold algorithm is used.

test_xgboost file is not running on windows 10 File "c:\Sander\my_code\rep-master\tests\test_xgboost.py", line 4, in from rep.estimators import XGBoostClassifier, XGBoostRegressor

ImportError: cannot import name XGBoostClassifier

when rep installatoin is ok but xgboost instal fails Microsoft Windows Version 10.0.10586 2015 Microsoft Corporation. All rights reserved.

c:\Sander>pip install rep --no-dependencies Collecting rep Downloading rep-0.6.5.tar.gz (72kB) 100% |################################| 81kB 511kB/s Building wheels for collected packages: rep Running setup.py bdist_wheel for rep ... done Stored in directory: C:\Users\Sander\AppData\Local\pip\Cache\wheels\db\ee\06\ac6e3f3ec208edaee29654f0b55ffaf2719a51de799c396b91 Successfully built rep Installing collected packages: rep Successfully installed rep-0.6.5 You are using pip version 8.1.0, however version 8.1.2 is available. You should consider upgrading via the 'python -m pip install --upgrade pip' command.

c:\Sander>pip install xgboost==0.4a30 Collecting xgboost==0.4a30 Downloading xgboost-0.4a30.tar.gz (753kB) 100% |################################| 757kB 553kB/s No files/directories in c:\users\sander\appdata\local\temp\pip-build-exobfm\xgboost\pip-egg-info (from PKG-INFO) You are using pip version 8.1.0, however version 8.1.2 is available. You should consider upgrading via the 'python -m pip install --upgrade pip' command.

c:\Sander>

Hi! Is there a way to install REP manually on Windows environment? When installing dependencies i get an error when installing gnureadline:

Error: this module is not meant to work on Windows (try pyreadline instead)

Is there a way to use pyreadline for windows uoosers?

It seems last docker release depricates boot2docker http://docs.docker.com/installation/mac/ "This release of Docker deprecates the Boot2Docker command line in favor of Docker Machine"

How to install REP with latest docker release?

after python setup.py install I run cd tests ; nosetests . it runs for long time and ends up with errors:

..Info in <TCanvas::Print>: png file /tmp/tmpBg1dar.png has been created
Error in <TFile::TFile>: file toy_datasets/toyMC_bck_mass.root does not exist
E..E.
======================================================================
ERROR: tests.z_test_notebook.test_notebooks_in_folder('/root/rep/howto/00-intro-ROOT.ipynb',)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File "/root/rep/rep/test/test_notebooks.py", line 43, in check_single_notebook
    raise RuntimeError(description)
RuntimeError: Cell failed: 'T.Draw("min_DOCA")
c1'

 Traceback:
---------------------------------------------------------------------------
ReferenceError                            Traceback (most recent call last)
<ipython-input-5-aa6c7320180d> in <module>()
----> 1 T.Draw("min_DOCA")
      2 c1

ReferenceError: attempt to access a null-pointer

What am I missing?

% docker run -ti yandex/rep:0.6.6 bash -lc 'pip install -U numpy; python -c "from matplotlib import pyplot as plt; plt.figure()"'
Activate: ROOT has been sourced. Environment settings are ready.
ROOTSYS=/root/miniconda/envs/rep_py2
Deactivate:Unsetting ROOT environment variables..
Activate: ROOT has been sourced. Environment settings are ready.
ROOTSYS=/root/miniconda/envs/rep_py2
Collecting numpy
  Downloading numpy-1.11.2-cp27-cp27mu-manylinux1_x86_64.whl (15.3MB)
    100% |################################| 15.3MB 46kB/s
Installing collected packages: numpy
  Found existing installation: numpy 1.10.4
    DEPRECATION: Uninstalling a distutils installed project (numpy) has been deprecated and will be removed in a future version. This is due to the fact that uninstalling a distutils project will only partially uninstall the project.
    Uninstalling numpy-1.10.4:
      Successfully uninstalled numpy-1.10.4
Successfully installed numpy-1.11.2
/root/miniconda/envs/rep_py2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
bash: line 1:   222 Illegal instruction     python -c "from matplotlib import pyplot as plt; plt.figure()"

it is useful if jupyter frontend disconnects during fit/predict execution.

might the following snippet be handy for such cases

class Stopwatch(object):
    def __enter__(self):
        self.t0 = datetime.datetime.now()
        return self

    def __exit__(self, type, value, traceback):
        self.t1 = datetime.datetime.now()

    def __repr__(self):
        return "delta: (%s)" % (self.t1 - self.t0)


with Stopwatch() as sfit:
    time.sleep(1)
with Stopwatch() as spredict:
    time.sleep(1)

print "fit:", sfit, "spredict:", spredict

Hi.

I had old REP docker version in ~/rep_container which started with run.sh script on 8080 port. I updated REP and it broke: sudo $REPDIR/run.sh worked, but I couldn't connect to localhost:8080 (connection refused). I've decided to update docker and REP according to new instructions: https://github.com/yandex/rep/wiki/Install-REP-with-Docker-(Linux).

1. I installed Docker, according to instructions.
2. netstat -anl | grep 8888 gave empty result
3. git checkout https://github.com/yandex/rep.git didn't work (pathspec did not match any file(s) known to git), so I used git clone instead.
4. First run of sudo make run was successful and installed container.
5. I rebooted and second sudo make run gave the following

docker run -ti --rm -p 8888:8888 --name rep yandex/rep:0.6.4
Error response from daemon: Conflict. The name "rep" is already in use by container 3af0884aeedb. You have to remove (or rename) that container to be able to reuse that name. make: ** [run] Error 1* 6. I ran sudo docker images

REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE yandex/rep 0.6.4 18a48bc5a3b6 8 hours ago 2.635 GB anaderi/rep latest 63c3db2850b6 4 months ago 1.649 GB 91c95931e552 7 months ago 910 B 7. I tried sudo docker start rep. It worked and I opned REP on localhost:8888. But its working folder changed. Now it is /var/lib/docker/volumes/dbcc7ff99538007d9c6b244fb6b8f03bdcfd564f6076b36d79fa3330d2041107/_data/. It is quite unhandy, because it requires superuser rights to access and not conveniently located at all.

Question: Is it a new system or did I something wrong? If latter, how to I fix it and run REP container in handy folder?

Bumps notebook from 4.2.1 to 6.4.12.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Issue:

ModuleNotFoundError Traceback (most recent call last) in 5 from sklearn.ensemble import HistGradientBoostingClassifier 6 from rep.report.metrics import RocAuc ----> 7 from rep.metaml import GridOptimalSearchCV, FoldingScorer, RandomParameterOptimizer 8 from rep.estimators import SklearnClassifier

~/.local/lib/python3.8/site-packages/rep/metaml/init.py in 2 3 from .factory import ClassifiersFactory, RegressorsFactory ----> 4 from .folding import FoldingClassifier, FoldingRegressor 5 from .gridsearch import GridOptimalSearchCV 6 from .stacking import FeatureSplitter

~/.local/lib/python3.8/site-packages/rep/metaml/folding.py in 11 12 from sklearn import clone ---> 13 from sklearn.cross_validation import KFold 14 from sklearn.utils import check_random_state 15 from . import utils

ModuleNotFoundError: No module named 'sklearn.cross_validation'

Correction suggested based on https://stackoverflow.com/questions/30667525/importerror-no-module-named-sklearn-cross-validation

Bumps requests from 2.9.1 to 2.20.0.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Hi all,

first of all, I wanted to thank and compliment the developers for this brilliant library. I finally had the chance to start playing with it today, but I was stopped in my tracks when trying to use a TMVAClassifier:

AssertionError: ERROR: TMVA process is incorrect finished 
 LOG: None 
 Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/ludo/miniconda3/envs/pyroot/lib/python2.7/site-packages/rep/estimators/_tmvaFactory.py", line 86, in main
    tmva_process(classifier, info, data, labels, sample_weight)
  File "/home/ludo/miniconda3/envs/pyroot/lib/python2.7/site-packages/rep/estimators/_tmvaFactory.py", line 40, in tmva_process
    factory.AddVariable(var)
AttributeError: 'Factory' object has no attribute 'AddVariable'

My ROOT/TMVA versions are:

You are running ROOT Version: 6.08/00, Nov 4, 2016
TMVA Version 4.2.1, Feb 5, 2015

Searching the web for this error message led me to this post on the ROOT forum: https://root-forum.cern.ch/t/25090, where the cause of problem is indicated as being due to a breaking change in the TMVA API:

In recent ROOT versions (6.06 or 6.08, don't remember exactly), the TMVA interface has changed. You need to create a TMVA::DataLoader and call AddVariable on the dataloader object.

As I understand, this is related to what was mentioned by @gandreassi in a comment to #104. Any idea on how complicated it would be to adapt tmva_process to the new interface?