ngoix / ocrf Goto Github PK
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I installed Conda with anaconda2 and Windows Subsystem for Linux.
I have strictly followed the steps in instructions, and tried the example named bench_ocrf,py
Here is the error information:
DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
"This module will be removed in 0.20.", DeprecationWarning)
/home/sy/anaconda/envs/OCRF_ENV/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
DeprecationWarning)
OMP: Error #100: Fatal system error detected.
OMP: System error #22: Invalid argument
Aborted (core dumped)
...
cdef inline DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
ITYPE_t size) except -1 with gil:
cdef np.ndarray x1arr
cdef np.ndarray x2arr
with gil:
^
sklearn/neighbors/dist_metrics.pyx:1111:13: Trying to acquire the GIL while it is already held.
Traceback (most recent call last):
File "/home/rbrault/OCRF/build_tools/cythonize.py", line 198, in
main(root_dir_arg)
File "/home/rbrault/OCRF/build_tools/cythonize.py", line 190, in main
check_and_cythonize(root_dir)
File "/home/rbrault/OCRF/build_tools/cythonize.py", line 182, in check_and_cythonize
cythonize_if_unchanged(cur_dir, cython_file, gen_file, hashes)
File "/home/rbrault/OCRF/build_tools/cythonize.py", line 156, in cythonize_if_unchanged
cythonize(full_cython_path, full_gen_file_path)
File "/home/rbrault/OCRF/build_tools/cythonize.py", line 75, in cythonize
raise Exception('Cythonizing %s failed' % cython_file)
Exception: Cythonizing sklearn/neighbors/dist_metrics.pyx failed
Traceback (most recent call last):
File "setup.py", line 315, in
setup_package()
File "setup.py", line 299, in setup_package
generate_cython()
File "setup.py", line 185, in generate_cython
raise RuntimeError("Running cythonize failed!")
RuntimeError: Running cythonize failed!
Need investigation.
Linux-4.10.0-40-generic-x86_64-with-debian-stretch-sid
Python 3.6.3 |Anaconda custom (64-bit)| (default, Oct 16 2017, 15:28:36)
[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]
NumPy 1.13.3
SciPy 0.19.1
GCC 7.0.1
Related issue:
https://stackoverflow.com/questions/46920490/sklearn-installation-trying-to-acquire-the-gil-while-it-is-already-held
I am following your installation directions:
After running python setup.py install in python 2 environment I am getting this error:
Partial import of sklearn during the build process.
Generating cython files
Cythonizing sources
sklearn
sklearn/_isotonic.pyx has not changed
sklearn/__check_build/_check_build.pyx has not changed
sklearn/cluster/_dbscan_inner.pyx has not changed
Processing sklearn/cluster/_hierarchical.pyx
...
from libcpp.map cimport map as cpp_map
cimport numpy as np
sklearn/utils/fast_dict.pxd:13:10: cimported module has no attribute 'float64'
Traceback (most recent call last):
File "/home/javad/OCRF/build_tools/cythonize.py", line 198, in
main(root_dir_arg)
File "/home/javad/OCRF/build_tools/cythonize.py", line 190, in main
check_and_cythonize(root_dir)
File "/home/javad/OCRF/build_tools/cythonize.py", line 182, in check_and_cythonize
cythonize_if_unchanged(cur_dir, cython_file, gen_file, hashes)
File "/home/javad/OCRF/build_tools/cythonize.py", line 156, in cythonize_if_unchanged
cythonize(full_cython_path, full_gen_file_path)
File "/home/javad/OCRF/build_tools/cythonize.py", line 75, in cythonize
raise Exception('Cythonizing %s failed' % cython_file)
Exception: Cythonizing sklearn/cluster/_hierarchical.pyx failed
Traceback (most recent call last):
File "setup.py", line 315, in
setup_package()
File "setup.py", line 299, in setup_package
generate_cython()
File "setup.py", line 185, in generate_cython
raise RuntimeError("Running cythonize failed!")
RuntimeError: Running cythonize failed!
(py27) [javad@br005 OCRF]$
Hello everybody,
I'm new to GitHub, so please excuse me if I've forgotten any of the usual information.
I'm following the EM_MV example (https://github.com/ngoix/EMMV_benchmarks). Since these methods use the one_class_data, I first wanted to install the OCRF.
I was able to run all the commands of the installation instructions without any problems - except for the last one: python setup.py install
Here I got the following error:
Cython is installed with the latest version:
(OCRF_env) Gerrits-Air:OCRF gerritschumann$ pip install --upgrade Cython
Result: Requirement already up-to-date: Cython in /Users/gerritschumann/anaconda3/envs/OCRF_env/lib/python2.7/site-packages (0.29.13)
To my knowledge, no other Cython versions are installed:
(OCRF_env) Gerrits-Air:OCRF gerritschumann$ python -c "import cython; print(cython.__version__)"
Result: 0.29.13
If relevant: I already had Python 3.6.9 installed on my Macbook. Due to the OCRF installation, I now have Python 2.7. as environment.
If there is no quick fix for the problem, is there any way to reconstruct the output of one_class_data so that I can play through the EM_MV example?
It would really help me a lot! Many Thanks in advance!
I have downloaded the code and installed the library with Conda following the instructions.
I tested both libraries of Isolation forest in this project (IsolationforestOCRF) and Isolation forest installed in the root environment of Conda (IsolationforestSklearn).
Regarding the issues listed above, is there any problem with the implementation of OCRF and IsolationforestOCRF in this package?
Hi,
My issue doesn't point out a specific problem, I just have one question about the OCRF unsupervised ROC and PR results (cf. bench_ocrf_roc_pr_unsupervised_factorized.png).
I presumed that this OCRF method refers to the "OCRFsampling" (Désir et al., 2013).
But I was wondering how these results generated with the bench_ocrf.py file, are supposed to be unsupervised since the fit_predict
fonction needs the y_train
and y_test
(cf line 97 : scoring = model.fit_predict(X_train, y_train, X_test, y_test)
).
Thank you for your help.
Best regards,
MlleBerthe
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