Extremely simple and fast extreme multi-class and multi-label classifiers.

Overview

napkinXC

C++ build Python build Documentation Status PyPI version

napkinXC is an extremely simple and fast library for extreme multi-class and multi-label classification, that focus of implementing various methods for Probabilistic Label Trees. It allows training a classifier for very large datasets in just a few lines of code with minimal resources.

Right now, napkinXC implements the following features both in Python and C++:

  • Probabilistic Label Trees (PLTs) and Hierarchical softmax (HSM),
  • different type of inference methods (top-k, above given threshold, etc.),
  • fast prediction with labels weight, e.g., propensity scores,
  • efficient online F-measure optimization (OFO) procedure,
  • different tree building methods, including hierarchical k-means clustering method,
  • training of tree node
  • support for custom tree structures, and node weights,
  • helpers to download and load data from XML Repository,
  • helpers to measure performance (precisio[email protected], [email protected], [email protected], propensity-scored [email protected], and more).

Please note that this library is still under development and also serves as a base for experiments. API may not be compatible between releases and some of the experimental features may not be documented. Do not hesitate to open an issue in case of a question or problem!

The napkinXC is distributed under MIT license. All contributions to the project are welcome!

Python Quick Start and Documentation

Python (3.5+) version of napkinXC can be easily installed from PyPy repository on Linux and MacOS, it requires modern C++17 compiler, CMake and Git installed:

pip install napkinxc

or the latest master version directly from the GitHub repository (not recommended):

pip install git+https://github.com/mwydmuch/napkinXC.git

Minimal example of usage:

from napkinxc.datasets import load_dataset
from napkinxc.models import PLT
from napkinxc.measures import precision_at_k

X_train, Y_train = load_dataset("eurlex-4k", "train")
X_test, Y_test = load_dataset("eurlex-4k", "test")
plt = PLT("eurlex-model")
plt.fit(X_train, Y_train)
Y_pred = plt.predict(X_test, top_k=1)
print(precision_at_k(Y_test, Y_pred, k=1)) 

More examples can be found under python/examples directory. napkinXC's documentation is available at https://napkinxc.readthedocs.io.

Executable

napkinXC can also be used as executable to train and evaluate models using a data in LIBSVM format. See documentation for more details.

References and acknowledgments

This library implements methods from following papers (see experiments directory for scripts to replicate the results):

Another implementation of PLT model is available in extremeText library, that implements approach described in this NeurIPS paper.

Comments
  • OOM/SegFault issues?

    OOM/SegFault issues?

    PLTs train extremely quickly using this implementation which is fantastic to see. However, I have run into a few issues when training on larger datasets:

    • There is no batching method by default, which then requires very large matrices to be held in matrices in order to train the model. I assume the way to avoid this in memory issue is FitOnFile
    • Even if the training data fits comfortably in memory, at larger sizes such as >1 million training data points with >10k labels, the Python kernel crashes which I assume is due to an OOM or s error on the C++ side. It feels like there must be a memory leak somewhere, as the actual trees themselves never get that large, and I assume internally that the model trains in batches as outlined in the paper
    bug 
    opened by ASharmaML 3
  • feature dimension mismatch between train and test data

    feature dimension mismatch between train and test data

    Hi,

    There seems to be a bug in the data loading process.

    For example:

    from napkinxc.datasets import load_dataset
    trn_X, _ = load_dataset('wiki10-31k', 'train')
    tst_X, _ = load_dataset('wiki10-31k', 'test')
    print('# of features of training data', trn_X.shape[1])
    print('# of features of test data', tst_X.shape[1])
    

    gives:

    # of features of training data 101938
    # of features of test data 101937
    

    Cheers, Han

    opened by xiaohan2012 3
  • C++ compilation error when building

    C++ compilation error when building

    Hi

    I'm trying to install the latest version using pip install git+https://github.com/mwydmuch/napkinXC.git, which gives the following compilation error:

      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp: In function ‘void solve_l2r_lr_dual(const problem*, float*, float, float, float, int)’:
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:1335:29: error: no matching function for call to ‘max(float&, double)’
          Gmax = max(Gmax, fabs(gp));
    

    Thanks for your time.

    PS:

    The full error message is:

      ERROR: Command errored out with exit status 1:
       command: /home/cloud-user/code/diverse-xml/.venv/bin/python3.8 /home/cloud-user/code/diverse-xml/.venv/lib/python3.8/site-packages/pip/_vendor/pep517/in_process/_in_process.py build_wheel /tmp/tmphqbldjxt
           cwd: /tmp/pip-req-build-hszug3a8
      Complete output (149 lines):
      running bdist_wheel
      running build
      running build_py
      -- downloading/updating pybind11
      -- pybind11 directory found, pulling...
      From https://github.com/pybind/pybind11
       * branch            master     -> FETCH_HEAD
      --
      fatal: A branch named 'tag_v2.6.2' already exists.
      CMake Warning at GitUtils.cmake:251 (message):
        pybind11 some error happens.
      Call Stack (most recent call first):
        CMakeLists.txt:92 (git_clone)
    
    
      -- pybind11 v2.9.0 dev1
      -- Configuring done
      -- Generating done
      -- Build files have been written to: /tmp/pip-req-build-hszug3a8/build
      [  3%] Building C object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/liblinear/blas/axpy.c.o
      [  7%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/args.cpp.o
      [ 10%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/base.cpp.o
      [ 14%] Building C object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/liblinear/blas/dot.c.o
      [ 17%] Building C object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/liblinear/blas/nrm2.c.o
      [ 21%] Building C object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/liblinear/blas/scal.c.o
      [ 25%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/liblinear/linear.cpp.o
      [ 28%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/liblinear/tron.cpp.o
      [ 32%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/log.cpp.o
      [ 35%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/main.cpp.o
      [ 39%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/measure.cpp.o
      [ 42%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/misc.cpp.o
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp: In function ‘void solve_l2r_lr_dual(const problem*, float*, float, float, float, int)’:
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:1335:29: error: no matching function for call to ‘max(float&, double)’
          Gmax = max(Gmax, fabs(gp));
                                   ^
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:16:36: note: candidate: template<class T> T max(T, T)
       template <class T> static inline T max(T x,T y) { return (x>y)?x:y; }
                                          ^
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:16:36: note:   template argument deduction/substitution failed:
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:1335:29: note:   deduced conflicting types for parameter ‘T’ (‘float’ and ‘double’)
          Gmax = max(Gmax, fabs(gp));
                                   ^
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp: In function ‘float calc_max_p(const problem*, const parameter*)’:
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:2363:38: error: no matching function for call to ‘max(float&, double)’
         max_p = max(max_p, fabs(prob->y[i]));
                                            ^
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:16:36: note: candidate: template<class T> T max(T, T)
       template <class T> static inline T max(T x,T y) { return (x>y)?x:y; }
                                          ^
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:16:36: note:   template argument deduction/substitution failed:
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:2363:38: note:   deduced conflicting types for parameter ‘T’ (‘float’ and ‘double’)
         max_p = max(max_p, fabs(prob->y[i]));
                                            ^
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp: In function ‘model* load_model(const char*)’:
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:3022:35: warning: format ‘%lf’ expects argument of type ‘double*’, but argument 3 has type ‘float*’ [-Wformat=]
        if (fscanf(_stream, _format, _var) != 1)\
                                         ^
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:3098:4: note: in expansion of macro ‘FSCANF’
          FSCANF(fp,"%lf",&bias);
          ^
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:3022:35: warning: format ‘%lf’ expects argument of type ‘double*’, but argument 3 has type ‘float*’ [-Wformat=]
        if (fscanf(_stream, _format, _var) != 1)\
                                         ^
      /tmp/pip-req-build-hszug3a8/src/liblinear/linear.cpp:3136:4: note: in expansion of macro ‘FSCANF’
          FSCANF(fp, "%lf ", &model_->w[i*nr_w+j]);
          ^
      [ 46%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/model.cpp.o
      python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/build.make:159: recipe for target 'python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/liblinear/linear.cpp.o' failed
      make[2]: *** [python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/liblinear/linear.cpp.o] Error 1
      make[2]: *** Waiting for unfinished jobs....
      In file included from /tmp/pip-req-build-hszug3a8/src/base.h:34:0,
                       from /tmp/pip-req-build-hszug3a8/src/base.cpp:27:
      /tmp/pip-req-build-hszug3a8/src/vector.h:216:25: warning: inline function ‘virtual Real AbstractVector::at(int) const’ used but never defined
           virtual inline Real at(int index) const = 0;
                               ^
      /tmp/pip-req-build-hszug3a8/src/vector.h:217:26: warning: inline function ‘virtual Real& AbstractVector::operator[](int)’ used but never defined
           virtual inline Real& operator[](int index) = 0;
                                ^
      In file included from /tmp/pip-req-build-hszug3a8/src/misc.h:35:0,
                       from /tmp/pip-req-build-hszug3a8/src/misc.cpp:30:
      /tmp/pip-req-build-hszug3a8/src/matrix.h: In instantiation of ‘void RMatrix<T>::appendRow(const U&, bool) [with U = std::vector<IVPair<float> >; T = SparseVector]’:
      /tmp/pip-req-build-hszug3a8/src/misc.cpp:96:35:   required from here
      /tmp/pip-req-build-hszug3a8/src/matrix.h:38:44: error: invalid initialization of non-const reference of type ‘SparseVector&’ from an rvalue of type ‘void’
               T& row = r.emplace_back(vec, sorted);
                                                  ^
      python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/build.make:229: recipe for target 'python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/misc.cpp.o' failed
      make[2]: *** [python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/__/__/__/src/misc.cpp.o] Error 1
      CMakeFiles/Makefile2:145: recipe for target 'python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/all' failed
      make[1]: *** [python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/all] Error 2
      Makefile:135: recipe for target 'all' failed
    
    opened by xiaohan2012 2
  • segmentation fault (possibly in kmeans)

    segmentation fault (possibly in kmeans)

    Hi,

    The following snippet gives segmentation fault on my machine:

    from napkinxc.models import PLT
    from napkinxc.datasets import load_dataset
    
    trn_X, trn_Y = load_dataset('eurlex-4k', "train", verbose=1)
    model = PLT('output/test', tree_type='hierarchicalKmeans',
                arity=32,
                seed=25,
                threads=4, verbose=1)
    model.fit(trn_X, trn_Y)
    

    The output is:

    napkinXC 0.5.1 - train
      Model: output/test
        Type: plt
      Base models optimizer: liblinear
        Solver: L2R_LR_DUAL, eps: 0.1, cost: 10, max iter: 100, weights threshold: 0.1
      Tree type: hierarchicalKmeans, arity: 32, k-means eps: 0.0001, balanced: 1, weighted features: 0, max leaves: 100
      Threads: 4, memory limit: ~29G
      Seed: 25
    Building tree ...
    Computing labels' features matrix in 4 threads ...
    Hierarchical K-Means clustering in 4 threads ...
    [2]    20767 segmentation fault (core dumped)  python mwe.py
    

    Would it be possible to fix this?

    Other info:

    • napkinXC 0.5.1
    • Python 3.8.3
    • Ubuntu 16.04.7 LTS
    bug 
    opened by xiaohan2012 2
  • Support for custom tree (tree_structure in python interface)

    Support for custom tree (tree_structure in python interface)

    Hi,

    Thanks for writing this software, which is very useful!

    I'm currently experimenting with the effect of label trees and wish to load trees from file.

    Is it possible to pass a string to thetree_structure parameter in models.PLT class, so that a custom tree can be loaded? It seems like the current Python interface does not support it.

    If possible, I can make a pull request, and it would be nice if some instructions can be given, e.g., where and what to modify.

    Cheers, Han

    question 
    opened by xiaohan2012 2
  • a possible bug during kmeans initialization

    a possible bug during kmeans initialization

    Hi,

    During kmeans initialization, the randomly generated row index can produce segfault.

    https://github.com/mwydmuch/napkinXC/blob/6783031fc3ffe769092dc63292bb7f8857b73967/src/models/kmeans.cpp#L54

    https://github.com/mwydmuch/napkinXC/blob/6783031fc3ffe769092dc63292bb7f8857b73967/src/models/kmeans.cpp#L56 I suppose it should be:

     std::uniform_int_distribution<int> dist(0, points - 1); 
    

    Regards, Han

    bug 
    opened by xiaohan2012 1
  • string

    string "amazontitles-3M" to "amazontitles-3m" in datasets.py

    Hi,

    the following code is giving an error:

    from napkinxc.datasets import load_dataset
    
    _ = load_dataset('AmazonTitles-3M', 'train')
    
    ValueError: Dataset AmazonTitles-3M is not available
    

    It should be easy to fix: just change the string amazontitles-3M to amazontitles-3m in the file python/napkinxc/datasets.py

    bug 
    opened by xiaohan2012 1
  • Seg Fault Fix

    Seg Fault Fix

    Hello! While trying to do OFO using the python interface I was running into some segmentation faults happening at random times while doing Macro OFO. I went into the C++ code and I think I found the culprit, which was a pre-increment counter being used instead of the counter itself, which would result in the code trying to access memory outside the array's limits. When I tried running it again after making this change, I stopped getting seg faults and the OFO score actually improved. Disclaimer: I am quite rusty with C++ so please double check.

    opened by atriantafybbc 1
  • predict_proba was not returning probabilities

    predict_proba was not returning probabilities

    The model.predict_proba was not returning probabilities as it was using the internal _model.predict() function instead of the _model.predict_proba() function.

    opened by atriantafybbc 1
  • pip install napkinxc failed

    pip install napkinxc failed

    I've been trying to install napkinxc via pip and have repeatedly run into the below error. Any idea what could be wrong? Thanks.

    (base) atl436user1:~ user.name$ pip install napkinxc Collecting napkinxc Using cached napkinxc-0.4.0.tar.gz (142 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing wheel metadata ... done Requirement already satisfied: sklearn in ./anaconda3/lib/python3.7/site-packages/sklearn-0.0-py3.7.egg (from napkinxc) (0.0) Requirement already satisfied: scipy in ./anaconda3/lib/python3.7/site-packages (from napkinxc) (1.3.1) Requirement already satisfied: numpy in ./anaconda3/lib/python3.7/site-packages (from napkinxc) (1.18.1) Requirement already satisfied: scikit-learn in ./anaconda3/lib/python3.7/site-packages (from sklearn->napkinxc) (0.22.1) Requirement already satisfied: joblib>=0.11 in ./anaconda3/lib/python3.7/site-packages (from scikit-learn->sklearn->napkinxc) (0.14.1) Building wheels for collected packages: napkinxc Building wheel for napkinxc (PEP 517) ... error ERROR: Command errored out with exit status 1: command: /Users/alec.delany/anaconda3/bin/python /Users/user.name/anaconda3/lib/python3.7/site-packages/pip/_vendor/pep517/_in_process.py build_wheel /var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/tmpogk81v8q cwd: /private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-install-4bhkxsrq/napkinxc Complete output (106 lines): running bdist_wheel running build running build_py -- downloading/updating pybind11 -- pybind11 directory found, pulling... From https://github.com/pybind/pybind11

    • branch master -> FETCH_HEAD -- Already on 'master' -- Your branch is up to date with 'origin/master'.

    -- pybind11 v2.6.0 -- Configuring done -- Generating done -- Build files have been written to: /private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-install-4bhkxsrq/napkinxc/build Scanning dependencies of target pynxc [ 6%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/base.cpp.o [ 9%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/args.cpp.o [ 15%] Building C object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/blas/daxpy.c.o [ 15%] Building C object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/blas/dnrm2.c.o [ 15%] Building C object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/blas/ddot.c.o [ 21%] Building C object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/blas/dscal.c.o [ 21%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/data_reader.cpp.o [ 31%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/data_readers/vw_reader.cpp.o [ 31%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/data_readers/libsvm_reader.cpp.o [ 31%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/liblinear/linear.cpp.o [ 34%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/liblinear/tron.cpp.o [ 37%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/log.cpp.o [ 40%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/main.cpp.o [ 43%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/measure.cpp.o [ 46%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/misc.cpp.o [ 50%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/model.cpp.o [ 53%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/models/br.cpp.o [ 56%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/models/extreme_text.cpp.o [ 59%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/models/hsm.cpp.o In file included from /private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-install-4bhkxsrq/napkinxc/src/model.cpp:36: /private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-install-4bhkxsrq/napkinxc/src/models/online_plt.h:46:10: error: 'shared_timed_mutex' is unavailable: introduced in macOS 10.12 std::shared_timed_mutex treeMtx; ^ /Library/Developer/CommandLineTools/usr/bin/../include/c++/v1/shared_mutex:205:58: note: 'shared_timed_mutex' has been explicitly marked unavailable here class _LIBCPP_TYPE_VIS _LIBCPP_AVAILABILITY_SHARED_MUTEX shared_timed_mutex ^ [ 62%] Building CXX object python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir////src/models/kmeans.cpp.o 1 error generated. make[2]: *** [python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir///__/src/model.cpp.o] Error 1 make[2]: *** Waiting for unfinished jobs.... make[1]: *** [python/napkinxc/_napkinxc/CMakeFiles/pynxc.dir/all] Error 2 make: *** [all] Error 2

    [cmake] configuring CMake project...

    running build_py (cmake)

    [cmake] building CMake project -> build

    Traceback (most recent call last): File "/Users/user.name/anaconda3/lib/python3.7/site-packages/pip/_vendor/pep517/_in_process.py", line 280, in main() File "/Users/user.name/anaconda3/lib/python3.7/site-packages/pip/_vendor/pep517/_in_process.py", line 263, in main json_out['return_val'] = hook(**hook_input['kwargs']) File "/Users/user.name/anaconda3/lib/python3.7/site-packages/pip/_vendor/pep517/_in_process.py", line 205, in build_wheel metadata_directory) File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/setuptools/build_meta.py", line 217, in build_wheel wheel_directory, config_settings) File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/setuptools/build_meta.py", line 202, in _build_with_temp_dir self.run_setup() File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/setuptools/build_meta.py", line 254, in run_setup self).run_setup(setup_script=setup_script) File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/setuptools/build_meta.py", line 145, in run_setup exec(compile(code, file, 'exec'), locals()) File "setup.py", line 64, in include_package_data=True, File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/cmaketools/init.py", line 98, in setup _setup(**setup_args) File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/setuptools/init.py", line 153, in setup return distutils.core.setup(**attrs) File "/Users/user.name/anaconda3/lib/python3.7/distutils/core.py", line 148, in setup dist.run_commands() File "/Users/user.name/anaconda3/lib/python3.7/distutils/dist.py", line 966, in run_commands self.run_command(cmd) File "/Users/user.name/anaconda3/lib/python3.7/distutils/dist.py", line 985, in run_command cmd_obj.run() File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/wheel/bdist_wheel.py", line 290, in run self.run_command('build') File "/Users/user.name/anaconda3/lib/python3.7/distutils/cmd.py", line 313, in run_command self.distribution.run_command(command) File "/Users/user.name/anaconda3/lib/python3.7/distutils/dist.py", line 985, in run_command cmd_obj.run() File "/Users/user.name/anaconda3/lib/python3.7/distutils/command/build.py", line 135, in run self.run_command(cmd_name) File "/Users/user.name/anaconda3/lib/python3.7/distutils/cmd.py", line 313, in run_command self.distribution.run_command(command) File "/Users/user.name/anaconda3/lib/python3.7/distutils/dist.py", line 985, in run_command cmd_obj.run() File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/cmaketools/cmakecommands.py", line 110, in run self._run_cmake() File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/cmaketools/cmakecommands.py", line 104, in _run_cmake pkg_version=self.distribution.get_version(), File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/cmaketools/cmakebuilder.py", line 349, in run env=env, File "/private/var/folders/p9/mx3qb0ms6_gf8jx7tq82xr14s6lmfx/T/pip-build-env-evty4816/overlay/lib/python3.7/site-packages/cmaketools/cmakeutil.py", line 169, in build return sp.run(args, env=env).check_returncode() File "/Users/user.name/anaconda3/lib/python3.7/subprocess.py", line 422, in check_returncode self.stderr) subprocess.CalledProcessError: Command '['cmake', '--build', 'build', '-j', '7', '--config', 'Release']' returned non-zero exit status 2.

    ERROR: Failed building wheel for napkinxc Failed to build napkinxc ERROR: Could not build wheels for napkinxc which use PEP 517 and cannot be installed directly

    Running python 3.7.1 on MacOS Catalina version 10.15.6.

    opened by adelany3 1
  • build failed

    build failed

    [email protected]:~/ub16_prj/napkinXML$ make [ 6%] Building CXX object CMakeFiles/nxml.dir/src/main.cpp.o In file included from /home/ub16hp/ub16_prj/napkinXML/src/main.cpp:8:0: /home/ub16hp/ub16_prj/napkinXML/src/base.h: In member function ‘double Base::predictLoss(U*)’: /home/ub16hp/ub16_prj/napkinXML/src/base.h:65:25: error: ‘pow’ is not a member of ‘std’ if(hingeLoss) val = std::pow(fmax(0, 1 - val), 2); // Hinge squared loss ^ /home/ub16hp/ub16_prj/napkinXML/src/base.h:65:49: error: there are no arguments to ‘fmax’ that depend on a template parameter, so a declaration of ‘fmax’ must be available [-fpermissive] if(hingeLoss) val = std::pow(fmax(0, 1 - val), 2); // Hinge squared loss ^ /home/ub16hp/ub16_prj/napkinXML/src/base.h:65:49: note: (if you use ‘-fpermissive’, G++ will accept your code, but allowing the use of an undeclared name is deprecated) /home/ub16hp/ub16_prj/napkinXML/src/base.h:66:32: error: there are no arguments to ‘exp’ that depend on a template parameter, so a declaration of ‘exp’ must be available [-fpermissive] else val = log(1 + exp(-val)); // Log loss ^ /home/ub16hp/ub16_prj/napkinXML/src/base.h: In member function ‘double Base::predictProbability(U*)’: /home/ub16hp/ub16_prj/napkinXML/src/base.h:74:50: error: there are no arguments to ‘exp’ that depend on a template parameter, so a declaration of ‘exp’ must be available [-fpermissive] if(hingeLoss) val = 1.0 / (1.0 + exp(-2 * val)); // Probability for squared Hinge loss solver ^ /home/ub16hp/ub16_prj/napkinXML/src/base.h:75:37: error: there are no arguments to ‘exp’ that depend on a template parameter, so a declaration of ‘exp’ must be available [-fpermissive] else val = 1.0 / (1.0 + exp(-val)); // Probability ^ CMakeFiles/nxml.dir/build.make:86: recipe for target 'CMakeFiles/nxml.dir/src/main.cpp.o' failed make[2]: *** [CMakeFiles/nxml.dir/src/main.cpp.o] Error 1 CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/nxml.dir/all' failed make[1]: *** [CMakeFiles/nxml.dir/all] Error 2 Makefile:83: recipe for target 'all' failed make: *** [all] Error 2

    opened by loveJasmine 1
  • pickling models

    pickling models

    Hi,

    I'm trying to use napkinXC under ray, which relies pickle for data serialization.

    It seems that napkinXC models cannot be pickled.

    For instance,

    import pickle
    from napkinxc.models import PLT
    
    model = PLT('/tmp/something/')
    pickle.dump(model, open('/tmp/some-pickle.pkl', 'wb'))
    

    gives:

    TypeError: cannot pickle 'napkinxc._napkinxc.CPPModel' object.
    

    Is there any workaround or any plan to support pickling for this issue?

    opened by xiaohan2012 1
Releases(0.6.2)
Owner
Marek Wydmuch
Ph.D. student, machine learning and 3D graphics enthusiast
Marek Wydmuch
Revisiting Weakly Supervised Pre-Training of Visual Perception Models

SWAG: Supervised Weakly from hashtAGs This repository contains SWAG models from the paper Revisiting Weakly Supervised Pre-Training of Visual Percepti

Meta Research 134 Jan 05, 2023
Fermi Problems: A New Reasoning Challenge for AI

Fermi Problems: A New Reasoning Challenge for AI Fermi Problems are questions whose answer is a number that can only be reasonably estimated as a prec

AI2 15 May 28, 2022
Semiconductor Machine learning project

Wafer Fault Detection Problem Statement: Wafer (In electronics), also called a slice or substrate, is a thin slice of semiconductor, such as a crystal

kunal suryawanshi 1 Jan 15, 2022
Script that attempts to force M1 macs into RGB mode when used with monitors that are defaulting to YPbPr.

fix_m1_rgb Script that attempts to force M1 macs into RGB mode when used with monitors that are defaulting to YPbPr. No warranty provided for using th

Kevin Gao 116 Jan 01, 2023
Development of IP code based on VIPs and AADM

Sparse Implicit Processes In this repository we include the two different versions of the SIP code developed for the article Sparse Implicit Processes

1 Aug 22, 2022
The official repository for our paper "The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization".

Codebase for learning control flow in transformers The official repository for our paper "The Neural Data Router: Adaptive Control Flow in Transformer

Csordás Róbert 24 Oct 15, 2022
VQGAN+CLIP Colab Notebook with user-friendly interface.

VQGAN+CLIP and other image generation system VQGAN+CLIP Colab Notebook with user-friendly interface. Latest Notebook: Mse regulized zquantize Notebook

Justin John 227 Jan 05, 2023
Fast Axiomatic Attribution for Neural Networks (NeurIPS*2021)

Fast Axiomatic Attribution for Neural Networks This is the official repository accompanying the NeurIPS 2021 paper: R. Hesse, S. Schaub-Meyer, and S.

Visual Inference Lab @TU Darmstadt 11 Nov 21, 2022
Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking Part-Aware Measurement for Robust Multi-View Multi-Human 3D P

19 Oct 27, 2022
A new version of the CIDACS-RL linkage tool suitable to a cluster computing environment.

Fully Distributed CIDACS-RL The CIDACS-RL is a brazillian record linkage tool suitable to integrate large amount of data with high accuracy. However,

Robespierre Pita 5 Nov 04, 2022
Catch-all collection of generative art made using processing

Generative art with Processing.py Some art I have created for fun. Dependencies Processing for Python, see how to download/use here Packages contained

2 Mar 12, 2022
On the model-based stochastic value gradient for continuous reinforcement learning

On the model-based stochastic value gradient for continuous reinforcement learning This repository is by Brandon Amos, Samuel Stanton, Denis Yarats, a

Facebook Research 46 Dec 15, 2022
Yolov5 + Deep Sort with PyTorch

딥소트 수정중 Yolov5 + Deep Sort with PyTorch Introduction This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of obj

1 Nov 26, 2021
Official pytorch implementation of Active Learning for deep object detection via probabilistic modeling (ICCV 2021)

Active Learning for Deep Object Detection via Probabilistic Modeling This repository is the official PyTorch implementation of Active Learning for Dee

NVIDIA Research Projects 130 Jan 06, 2023
EMNLP 2021: Single-dataset Experts for Multi-dataset Question-Answering

MADE (Multi-Adapter Dataset Experts) This repository contains the implementation of MADE (Multi-adapter dataset experts), which is described in the pa

Princeton Natural Language Processing 68 Jul 18, 2022
A Data Annotation Tool for Semantic Segmentation, Object Detection and Lane Line Detection.(In Development Stage)

Data-Annotation-Tool How to Run this Tool? To run this software, follow the steps: git clone https://github.com/Autonomous-Car-Project/Data-Annotation

TiVRA AI 13 Aug 18, 2022
Training neural models with structured signals.

Neural Structured Learning in TensorFlow Neural Structured Learning (NSL) is a new learning paradigm to train neural networks by leveraging structured

955 Jan 02, 2023
Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.

WECHSEL Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. arXiv: https://arx

Institute of Computational Perception 45 Dec 29, 2022
Official PyTorch implementation of "Improving Face Recognition with Large AgeGaps by Learning to Distinguish Children" (BMVC 2021)

Inter-Prototype (BMVC 2021): Official Project Webpage This repository provides the official PyTorch implementation of the following paper: Improving F

Jungsoo Lee 16 Jun 30, 2022
Introducing neural networks to predict stock prices

IntroNeuralNetworks in Python: A Template Project IntroNeuralNetworks is a project that introduces neural networks and illustrates an example of how o

Vivek Palaniappan 637 Jan 04, 2023