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Loading Arff Data not working #57
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Hey there,
Available data sets can be listed like this:
For more information please see here. |
Not working here either, on Python 3.5 with Anaconda. First time, it failed with trying to load the Standard Library from future. Monkeypatched that import out with a comment. Now I'm getting this:
|
I think this is the same bytes/string issue regarding the incompatibility between Python 2 and 3. |
I can replicate this problem for a clean install of |
fixed dataset fetching and listing; closes #57
@ChristianSch thank you for the fix! |
* Added support for sparse X and y. Corrected small typo: "ytring" -> "ystring" * Added tests for sparse X and y. * Added a return state to the fit() method to comlpy with the usual interface of scikit-learn. * Cleanup: deleted unused variables, corrected variable name case, ... * fixed dataset fetching and listing; closes scikit-multilearn#57 * hotfix: removed standard library packages from requirements.txt to prevent typosquatting and malicious code execution * Fix np.zeros in rakelo.py (scikit-multilearn#76) This commit fixes the use of np.zeros, formerly np.zeroes, to resolve Issue scikit-multilearn#76 Author: ljvmiranda921 Email: ljvmiranda@gmail.com * add citation info test if slack commit log works * Update README.md (scikit-multilearn#74) This commit updates the README.md for this library. New sections were added: - A short description of the project - Features - Dependencies - Installation - Basic Usage - Contributing - Cite Two badges were also added: - PyPI badge - License badge Next steps (can only be done by project owner): - Add travis-ci badge once a successfull travis-build is implemented (owner only). - Add documentation badge (owner only) Author: ljvmiranda921 E-mail: ljvmiranda@gmail.com * initial travis setup * fix tests * remove pyc in travis * name container properly, pass MEKA_CLASSPATH * add travis * add slack notifications * trying multi-env travis * fixed igraph package name * add test requirements * add osx test build * fix linux py3 build name * fix osx homebrew repo name * add one more osx homebrew repo * enforce p3 on osx in travis * fix python osx problems on travis * pip2 instead of pip * cask remove oclint * pip3 instead of pip * explicit python3 on osx for travis * correc liac-arff req * add dtype to np.zeros * add a per label binarizer for quality measures, closes scikit-multilearn#84 * fix BRkNN top label number selection syntax error * mod fit for sparse y columns ensure that sparse y columns of shapes like (800,1) (returned from some classifiers etc) are converted properly to shape (800,) -otherwise bugs are thrown by some scikit validation functions. * proper processing of output matrix structures ensure proper back and forth conversions of y values shaped like (800,1) and (800,) - to avoid errors thrown by some scikit validation functions. np.ravel does not properly process some matrices unless they are first cast to arrays. * make the code much more readable * some more variables renamed to a more informative name * import issparse, reformat * import numpy * prediction transposition in CC is no longer required * fix returning 1d label vector and testing for that * fix meka io bytes/strings and decode if needed, reformat * enforce updating to current docker image * fix travis for meka tests * add self-edge normalization option and fix test * disable weight normalization on unweighted graph * separate graph builders and label space clusterers, more tests written, some parameter sets for graphtool do not work atm * formatting changes * don't build a list if noone needs it * fix some circularity problems with typing * less extensive data testing for now, a lot of cases fail with certain generator params, due to one-classiness of partitions * fix stochastic block modelling based on graphtool * fix and standardize clustering output alongside with a proper integration test in label space partitioning classifier * remove osx travis for now, not working anyway * adjust partitions to new test data * adjust labelset sizes to new test data * make sure correct version is pulled * change the default rakeld/rakelo behaviour to include labelpowerset, a voting classifier is added to allow overlapping classification in rakel style with any clusterer and clasifier, a rakel's clustering logic is moved to a random clusterer * fix CV base test * travis python2 should work correctly now, with new devdocker * introduce a working test case instead of randomly crashing generators * add absoulte imports to fix igraph import in p2 * fix tests and add set_params support for clusters so that CV works * some flaky setup line for travis * fix random label space clusterer test with overlaps to pass * temporary workaround for dense matrices * workaround output in matrix shape as well * update documentation * documentation and naming corrections * fix rename of cluster_*
* Added support for sparse X and y. Corrected small typo: "ytring" -> "ystring" * Added tests for sparse X and y. * Added a return state to the fit() method to comlpy with the usual interface of scikit-learn. * Cleanup: deleted unused variables, corrected variable name case, ... * fixed dataset fetching and listing; closes scikit-multilearn#57 * hotfix: removed standard library packages from requirements.txt to prevent typosquatting and malicious code execution * Fix np.zeros in rakelo.py (scikit-multilearn#76) This commit fixes the use of np.zeros, formerly np.zeroes, to resolve Issue scikit-multilearn#76 Author: ljvmiranda921 Email: ljvmiranda@gmail.com * add citation info test if slack commit log works * Update README.md (scikit-multilearn#74) This commit updates the README.md for this library. New sections were added: - A short description of the project - Features - Dependencies - Installation - Basic Usage - Contributing - Cite Two badges were also added: - PyPI badge - License badge Next steps (can only be done by project owner): - Add travis-ci badge once a successfull travis-build is implemented (owner only). - Add documentation badge (owner only) Author: ljvmiranda921 E-mail: ljvmiranda@gmail.com * implemented iterative stratification with high-order relationships support (Sechidis2011, Szymanski2017) * fix imports, random state and fold init * unit tests * add documentation * initial travis setup * fix tests * remove pyc in travis * name container properly, pass MEKA_CLASSPATH * add travis * add slack notifications * trying multi-env travis * fixed igraph package name * add test requirements * add osx test build * fix linux py3 build name * fix osx homebrew repo name * add one more osx homebrew repo * enforce p3 on osx in travis * fix python osx problems on travis * pip2 instead of pip * cask remove oclint * pip3 instead of pip * explicit python3 on osx for travis * correc liac-arff req * add dtype to np.zeros * add a per label binarizer for quality measures, closes scikit-multilearn#84 * fix BRkNN top label number selection syntax error * fix normalization of confidence computation normalize by number of neighbors counted, rather than label count (original produces results > 1 when k > num_labels.) * mod fit for sparse y columns ensure that sparse y columns of shapes like (800,1) (returned from some classifiers etc) are converted properly to shape (800,) -otherwise bugs are thrown by some scikit validation functions. * proper processing of output matrix structures ensure proper back and forth conversions of y values shaped like (800,1) and (800,) - to avoid errors thrown by some scikit validation functions. np.ravel does not properly process some matrices unless they are first cast to arrays. * make the code much more readable * some more variables renamed to a more informative name * import issparse, reformat * import numpy * prediction transposition in CC is no longer required * fix returning 1d label vector and testing for that * fix meka io bytes/strings and decode if needed, reformat * enforce updating to current docker image * fix travis for meka tests * add self-edge normalization option and fix test * disable weight normalization on unweighted graph * separate graph builders and label space clusterers, more tests written, some parameter sets for graphtool do not work atm * formatting changes * don't build a list if noone needs it * fix some circularity problems with typing * less extensive data testing for now, a lot of cases fail with certain generator params, due to one-classiness of partitions * fix stochastic block modelling based on graphtool * fix and standardize clustering output alongside with a proper integration test in label space partitioning classifier * remove osx travis for now, not working anyway * adjust partitions to new test data * adjust labelset sizes to new test data * make sure correct version is pulled * change the default rakeld/rakelo behaviour to include labelpowerset, a voting classifier is added to allow overlapping classification in rakel style with any clusterer and clasifier, a rakel's clustering logic is moved to a random clusterer * fix CV base test * travis python2 should work correctly now, with new devdocker * introduce a working test case instead of randomly crashing generators * add absoulte imports to fix igraph import in p2 * fix tests and add set_params support for clusters so that CV works * some flaky setup line for travis * fix random label space clusterer test with overlaps to pass * temporary workaround for dense matrices * workaround output in matrix shape as well * update documentation * documentation and naming corrections * fix rename of cluster_* * adhere to review * document helpers * see reqs/dev * clean up requirements * adhere to new docs convention * skip graphtool on windows * make external library imports optional * first approach at windows oriented CI * copy how requests did it * fix paths * more work on appveyor * change path delimeter for windows XD * one more path separator fix * add missing wrapper for windows * give up on cmd_in_env atm * skip igraph test on win32 atm * ignore java for a moment * fix cmdlet * more debugging of appveyor * more appveyor * don't build for now, just test * give up igraph/graphtool tests on win32 * fix test command on windows * more disabling of igraph and graphtool on win32 * fix the louvain community dependency * close file before removal, should fix meka on windows * setup slack notifications * fix yaml indent * add appveyor badge * save file name before closing
The first line from the official docs pertaining to loading datasets,
from skmultilearn.dataset import Dataset,
shows the error" ImportError: cannot import name 'Dataset' "
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