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I got this error after setting up all datasets but the mesp ones. Is it really an issue related to that?
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| collected 37 items | |
| tests/test_classification_metric.py .... [ 10%] | |
| tests/test_demo_adversarial_debiasing.py F [ 13%] | |
| tests/test_demo_calibrated_eqodds_postprocessing.py F [ 16%] | |
| tests/test_demo_lfr.py F [ 18%] | |
| tests/test_demo_optim_data_preproc.py F [ 21%] | |
| tests/test_demo_reject_option_classification.py F [ 24%] | |
| tests/test_demo_reweighing_preproc.py F [ 27%] | |
| tests/test_disparate_impact_remover.py FF [ 32%] | |
| tests/test_lfr.py ....... [ 51%] | |
| tests/test_meta_classifier.py . [ 54%] | |
| tests/test_reweighing.py . [ 56%] | |
| tests/test_sample_distortion_metric.py ...... [ 72%] | |
| tests/test_standard_datasets.py ..... [ 86%] | |
| tests/test_structured_dataset.py ..... [100%] | |
| =========================================================================== FAILURES =========================================================================== | |
| _______________________________________________________________ test_demo_adversarial_debiasing ________________________________________________________________ | |
| def test_demo_adversarial_debiasing(): | |
| nb, errors = notebook_run(os.path.join( | |
| os.path.dirname(os.path.abspath(__file__)), | |
| > '..', 'examples', 'demo_adversarial_debiasing.ipynb')) | |
| ../tests/test_demo_adversarial_debiasing.py:11: | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| ../tests/notebook_runner.py:33: in notebook_run | |
| nb = nbformat.read(fout, nbformat.current_nbformat) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:141: in read | |
| return reads(fp.read(), as_version, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:74: in reads | |
| nb = reader.reads(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:58: in reads | |
| nb_dict = parse_json(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:14: in parse_json | |
| nb_dict = json.loads(s, **kwargs) | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| s = b'{\n "cells": [\n {\n "cell_type": "markdown",\n "metadata": {},\n "source": [\n "#### This notebook demons...ython",\n "pygments_lexer": "ipython3",\n "version": "3.5.6"\n }\n },\n "nbformat": 4,\n "nbformat_minor": 2\n}\n' | |
| encoding = None, cls = None, object_hook = None, parse_float = None, parse_int = None, parse_constant = None, object_pairs_hook = None, kw = {} | |
| def loads(s, encoding=None, cls=None, object_hook=None, parse_float=None, | |
| parse_int=None, parse_constant=None, object_pairs_hook=None, **kw): | |
| """Deserialize ``s`` (a ``str`` instance containing a JSON | |
| document) to a Python object. | |
| ``object_hook`` is an optional function that will be called with the | |
| result of any object literal decode (a ``dict``). The return value of | |
| ``object_hook`` will be used instead of the ``dict``. This feature | |
| can be used to implement custom decoders (e.g. JSON-RPC class hinting). | |
| ``object_pairs_hook`` is an optional function that will be called with the | |
| result of any object literal decoded with an ordered list of pairs. The | |
| return value of ``object_pairs_hook`` will be used instead of the ``dict``. | |
| This feature can be used to implement custom decoders that rely on the | |
| order that the key and value pairs are decoded (for example, | |
| collections.OrderedDict will remember the order of insertion). If | |
| ``object_hook`` is also defined, the ``object_pairs_hook`` takes priority. | |
| ``parse_float``, if specified, will be called with the string | |
| of every JSON float to be decoded. By default this is equivalent to | |
| float(num_str). This can be used to use another datatype or parser | |
| for JSON floats (e.g. decimal.Decimal). | |
| ``parse_int``, if specified, will be called with the string | |
| of every JSON int to be decoded. By default this is equivalent to | |
| int(num_str). This can be used to use another datatype or parser | |
| for JSON integers (e.g. float). | |
| ``parse_constant``, if specified, will be called with one of the | |
| following strings: -Infinity, Infinity, NaN. | |
| This can be used to raise an exception if invalid JSON numbers | |
| are encountered. | |
| To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` | |
| kwarg; otherwise ``JSONDecoder`` is used. | |
| The ``encoding`` argument is ignored and deprecated. | |
| """ | |
| if not isinstance(s, str): | |
| raise TypeError('the JSON object must be str, not {!r}'.format( | |
| > s.__class__.__name__)) | |
| E TypeError: the JSON object must be str, not 'bytes' | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/json/__init__.py:312: TypeError | |
| --------------------------------------------------------------------- Captured stderr call --------------------------------------------------------------------- | |
| [NbConvertApp] Converting notebook /home/codeminer42/Code/open-source/aif360-learn/tests/../examples/demo_adversarial_debiasing.ipynb to notebook | |
| [NbConvertApp] Executing notebook with kernel: python3 | |
| [NbConvertApp] Writing 31479 bytes to /tmp/tmpnf6s8cj9.ipynb | |
| ____________________________________________________________ test_calibrated_eqodds_postprocessing _____________________________________________________________ | |
| def test_calibrated_eqodds_postprocessing(): | |
| nb, errors = notebook_run(os.path.join( | |
| os.path.dirname(os.path.abspath(__file__)), | |
| > '..', 'examples', 'demo_calibrated_eqodds_postprocessing.ipynb')) | |
| ../tests/test_demo_calibrated_eqodds_postprocessing.py:10: | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| ../tests/notebook_runner.py:33: in notebook_run | |
| nb = nbformat.read(fout, nbformat.current_nbformat) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:141: in read | |
| return reads(fp.read(), as_version, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:74: in reads | |
| nb = reader.reads(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:58: in reads | |
| nb_dict = parse_json(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:14: in parse_json | |
| nb_dict = json.loads(s, **kwargs) | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| s = b'{\n "cells": [\n {\n "cell_type": "markdown",\n "metadata": {},\n "source": [\n "#### This notebook demons...ython",\n "pygments_lexer": "ipython3",\n "version": "3.5.6"\n }\n },\n "nbformat": 4,\n "nbformat_minor": 2\n}\n' | |
| encoding = None, cls = None, object_hook = None, parse_float = None, parse_int = None, parse_constant = None, object_pairs_hook = None, kw = {} | |
| def loads(s, encoding=None, cls=None, object_hook=None, parse_float=None, | |
| parse_int=None, parse_constant=None, object_pairs_hook=None, **kw): | |
| """Deserialize ``s`` (a ``str`` instance containing a JSON | |
| document) to a Python object. | |
| ``object_hook`` is an optional function that will be called with the | |
| result of any object literal decode (a ``dict``). The return value of | |
| ``object_hook`` will be used instead of the ``dict``. This feature | |
| can be used to implement custom decoders (e.g. JSON-RPC class hinting). | |
| ``object_pairs_hook`` is an optional function that will be called with the | |
| result of any object literal decoded with an ordered list of pairs. The | |
| return value of ``object_pairs_hook`` will be used instead of the ``dict``. | |
| This feature can be used to implement custom decoders that rely on the | |
| order that the key and value pairs are decoded (for example, | |
| collections.OrderedDict will remember the order of insertion). If | |
| ``object_hook`` is also defined, the ``object_pairs_hook`` takes priority. | |
| ``parse_float``, if specified, will be called with the string | |
| of every JSON float to be decoded. By default this is equivalent to | |
| float(num_str). This can be used to use another datatype or parser | |
| for JSON floats (e.g. decimal.Decimal). | |
| ``parse_int``, if specified, will be called with the string | |
| of every JSON int to be decoded. By default this is equivalent to | |
| int(num_str). This can be used to use another datatype or parser | |
| for JSON integers (e.g. float). | |
| ``parse_constant``, if specified, will be called with one of the | |
| following strings: -Infinity, Infinity, NaN. | |
| This can be used to raise an exception if invalid JSON numbers | |
| are encountered. | |
| To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` | |
| kwarg; otherwise ``JSONDecoder`` is used. | |
| The ``encoding`` argument is ignored and deprecated. | |
| """ | |
| if not isinstance(s, str): | |
| raise TypeError('the JSON object must be str, not {!r}'.format( | |
| > s.__class__.__name__)) | |
| E TypeError: the JSON object must be str, not 'bytes' | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/json/__init__.py:312: TypeError | |
| --------------------------------------------------------------------- Captured stderr call --------------------------------------------------------------------- | |
| [NbConvertApp] Converting notebook /home/codeminer42/Code/open-source/aif360-learn/tests/../examples/demo_calibrated_eqodds_postprocessing.ipynb to notebook | |
| [NbConvertApp] Executing notebook with kernel: python3 | |
| [NbConvertApp] Writing 69824 bytes to /tmp/tmp2z2hztr7.ipynb | |
| ________________________________________________________________________ test_demo_lfr _________________________________________________________________________ | |
| def test_demo_lfr(): | |
| nb, errors = notebook_run(os.path.join( | |
| os.path.dirname(os.path.abspath(__file__)), | |
| > '..', 'examples', 'demo_lfr.ipynb')) | |
| ../tests/test_demo_lfr.py:11: | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| ../tests/notebook_runner.py:33: in notebook_run | |
| nb = nbformat.read(fout, nbformat.current_nbformat) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:141: in read | |
| return reads(fp.read(), as_version, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:74: in reads | |
| nb = reader.reads(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:58: in reads | |
| nb_dict = parse_json(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:14: in parse_json | |
| nb_dict = json.loads(s, **kwargs) | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| s = b'{\n "cells": [\n {\n "cell_type": "markdown",\n "metadata": {},\n "source": [\n "#### This notebook demons...ython",\n "pygments_lexer": "ipython3",\n "version": "3.5.6"\n }\n },\n "nbformat": 4,\n "nbformat_minor": 2\n}\n' | |
| encoding = None, cls = None, object_hook = None, parse_float = None, parse_int = None, parse_constant = None, object_pairs_hook = None, kw = {} | |
| def loads(s, encoding=None, cls=None, object_hook=None, parse_float=None, | |
| parse_int=None, parse_constant=None, object_pairs_hook=None, **kw): | |
| """Deserialize ``s`` (a ``str`` instance containing a JSON | |
| document) to a Python object. | |
| ``object_hook`` is an optional function that will be called with the | |
| result of any object literal decode (a ``dict``). The return value of | |
| ``object_hook`` will be used instead of the ``dict``. This feature | |
| can be used to implement custom decoders (e.g. JSON-RPC class hinting). | |
| ``object_pairs_hook`` is an optional function that will be called with the | |
| result of any object literal decoded with an ordered list of pairs. The | |
| return value of ``object_pairs_hook`` will be used instead of the ``dict``. | |
| This feature can be used to implement custom decoders that rely on the | |
| order that the key and value pairs are decoded (for example, | |
| collections.OrderedDict will remember the order of insertion). If | |
| ``object_hook`` is also defined, the ``object_pairs_hook`` takes priority. | |
| ``parse_float``, if specified, will be called with the string | |
| of every JSON float to be decoded. By default this is equivalent to | |
| float(num_str). This can be used to use another datatype or parser | |
| for JSON floats (e.g. decimal.Decimal). | |
| ``parse_int``, if specified, will be called with the string | |
| of every JSON int to be decoded. By default this is equivalent to | |
| int(num_str). This can be used to use another datatype or parser | |
| for JSON integers (e.g. float). | |
| ``parse_constant``, if specified, will be called with one of the | |
| following strings: -Infinity, Infinity, NaN. | |
| This can be used to raise an exception if invalid JSON numbers | |
| are encountered. | |
| To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` | |
| kwarg; otherwise ``JSONDecoder`` is used. | |
| The ``encoding`` argument is ignored and deprecated. | |
| """ | |
| if not isinstance(s, str): | |
| raise TypeError('the JSON object must be str, not {!r}'.format( | |
| > s.__class__.__name__)) | |
| E TypeError: the JSON object must be str, not 'bytes' | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/json/__init__.py:312: TypeError | |
| --------------------------------------------------------------------- Captured stdout call --------------------------------------------------------------------- | |
| RUNNING THE L-BFGS-B CODE | |
| * * * | |
| Machine precision = 2.220D-16 | |
| N = 131 M = 10 | |
| At X0 0 variables are exactly at the bounds | |
| At iterate 0 f= 2.08403D+04 |proj g|= 2.34747D+03 | |
| At iterate 1 f= 1.92268D+04 |proj g|= 2.26813D+03 | |
| At iterate 2 f= 1.87377D+04 |proj g|= 8.24798D+02 | |
| At iterate 3 f= 1.83557D+04 |proj g|= 1.06841D+03 | |
| At iterate 4 f= 1.68390D+04 |proj g|= 4.86068D+02 | |
| At iterate 5 f= 1.64426D+04 |proj g|= 1.25189D+03 | |
| At iterate 6 f= 1.63187D+04 |proj g|= 6.35896D+02 | |
| At iterate 7 f= 1.62482D+04 |proj g|= 3.71498D+02 | |
| At iterate 8 f= 1.60510D+04 |proj g|= 1.07705D+03 | |
| At iterate 9 f= 1.60067D+04 |proj g|= 9.86886D+02 | |
| At iterate 10 f= 1.59015D+04 |proj g|= 9.63617D+02 | |
| At iterate 11 f= 1.57740D+04 |proj g|= 2.59482D+02 | |
| At iterate 12 f= 1.57225D+04 |proj g|= 3.02092D+02 | |
| At iterate 13 f= 1.55402D+04 |proj g|= 9.75247D+02 | |
| At iterate 14 f= 1.54094D+04 |proj g|= 3.32286D+02 | |
| At iterate 15 f= 1.53602D+04 |proj g|= 3.75996D+02 | |
| At iterate 16 f= 1.53134D+04 |proj g|= 4.32137D+02 | |
| At iterate 17 f= 1.52420D+04 |proj g|= 3.38511D+02 | |
| At iterate 18 f= 1.51964D+04 |proj g|= 1.44210D+02 | |
| At iterate 19 f= 1.51835D+04 |proj g|= 8.54599D+01 | |
| At iterate 20 f= 1.51658D+04 |proj g|= 1.33628D+02 | |
| At iterate 21 f= 1.51340D+04 |proj g|= 2.38552D+02 | |
| At iterate 22 f= 1.51055D+04 |proj g|= 3.40586D+02 | |
| At iterate 23 f= 1.50862D+04 |proj g|= 2.09937D+02 | |
| At iterate 24 f= 1.50819D+04 |proj g|= 8.90523D+01 | |
| At iterate 25 f= 1.50802D+04 |proj g|= 1.01242D+02 | |
| At iterate 26 f= 1.50761D+04 |proj g|= 1.11889D+02 | |
| At iterate 27 f= 1.50663D+04 |proj g|= 1.50153D+02 | |
| At iterate 28 f= 1.50551D+04 |proj g|= 8.58364D+01 | |
| At iterate 29 f= 1.50504D+04 |proj g|= 9.95440D+01 | |
| At iterate 30 f= 1.50470D+04 |proj g|= 5.58267D+01 | |
| At iterate 31 f= 1.50438D+04 |proj g|= 4.11067D+01 | |
| At iterate 32 f= 1.50386D+04 |proj g|= 5.29101D+01 | |
| At iterate 33 f= 1.50296D+04 |proj g|= 6.60456D+01 | |
| At iterate 34 f= 1.50184D+04 |proj g|= 7.37458D+01 | |
| At iterate 35 f= 1.50111D+04 |proj g|= 3.21202D+02 | |
| At iterate 36 f= 1.50027D+04 |proj g|= 1.00139D+02 | |
| * * * | |
| Tit = total number of iterations | |
| Tnf = total number of function evaluations | |
| Tnint = total number of segments explored during Cauchy searches | |
| Skip = number of BFGS updates skipped | |
| Nact = number of active bounds at final generalized Cauchy point | |
| Projg = norm of the final projected gradient | |
| F = final function value | |
| * * * | |
| N Tit Tnf Tnint Skip Nact Projg F | |
| 131 36 38 45 0 4 1.001D+02 1.500D+04 | |
| F = 15002.718090290677 | |
| STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT | |
| Cauchy time 0.000E+00 seconds. | |
| Subspace minimization time 0.000E+00 seconds. | |
| Line search time 0.000E+00 seconds. | |
| Total User time 0.000E+00 seconds. | |
| --------------------------------------------------------------------- Captured stderr call --------------------------------------------------------------------- | |
| [NbConvertApp] Converting notebook /home/codeminer42/Code/open-source/aif360-learn/tests/../examples/demo_lfr.ipynb to notebook | |
| [NbConvertApp] Executing notebook with kernel: python3 | |
| [NbConvertApp] Writing 32291 bytes to /tmp/tmp6g2pv_y_.ipynb | |
| ______________________________________________________________________ test_optim_preproc ______________________________________________________________________ | |
| def test_optim_preproc(): | |
| nb, errors = notebook_run(os.path.join( | |
| os.path.dirname(os.path.abspath(__file__)), | |
| > '..', 'examples', 'demo_optim_data_preproc.ipynb')) | |
| ../tests/test_demo_optim_data_preproc.py:11: | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| ../tests/notebook_runner.py:33: in notebook_run | |
| nb = nbformat.read(fout, nbformat.current_nbformat) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:141: in read | |
| return reads(fp.read(), as_version, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:74: in reads | |
| nb = reader.reads(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:58: in reads | |
| nb_dict = parse_json(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:14: in parse_json | |
| nb_dict = json.loads(s, **kwargs) | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| s = b'{\n "cells": [\n {\n "cell_type": "markdown",\n "metadata": {},\n "source": [\n "#### This notebook demons...ython",\n "pygments_lexer": "ipython3",\n "version": "3.5.6"\n }\n },\n "nbformat": 4,\n "nbformat_minor": 2\n}\n' | |
| encoding = None, cls = None, object_hook = None, parse_float = None, parse_int = None, parse_constant = None, object_pairs_hook = None, kw = {} | |
| def loads(s, encoding=None, cls=None, object_hook=None, parse_float=None, | |
| parse_int=None, parse_constant=None, object_pairs_hook=None, **kw): | |
| """Deserialize ``s`` (a ``str`` instance containing a JSON | |
| document) to a Python object. | |
| ``object_hook`` is an optional function that will be called with the | |
| result of any object literal decode (a ``dict``). The return value of | |
| ``object_hook`` will be used instead of the ``dict``. This feature | |
| can be used to implement custom decoders (e.g. JSON-RPC class hinting). | |
| ``object_pairs_hook`` is an optional function that will be called with the | |
| result of any object literal decoded with an ordered list of pairs. The | |
| return value of ``object_pairs_hook`` will be used instead of the ``dict``. | |
| This feature can be used to implement custom decoders that rely on the | |
| order that the key and value pairs are decoded (for example, | |
| collections.OrderedDict will remember the order of insertion). If | |
| ``object_hook`` is also defined, the ``object_pairs_hook`` takes priority. | |
| ``parse_float``, if specified, will be called with the string | |
| of every JSON float to be decoded. By default this is equivalent to | |
| float(num_str). This can be used to use another datatype or parser | |
| for JSON floats (e.g. decimal.Decimal). | |
| ``parse_int``, if specified, will be called with the string | |
| of every JSON int to be decoded. By default this is equivalent to | |
| int(num_str). This can be used to use another datatype or parser | |
| for JSON integers (e.g. float). | |
| ``parse_constant``, if specified, will be called with one of the | |
| following strings: -Infinity, Infinity, NaN. | |
| This can be used to raise an exception if invalid JSON numbers | |
| are encountered. | |
| To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` | |
| kwarg; otherwise ``JSONDecoder`` is used. | |
| The ``encoding`` argument is ignored and deprecated. | |
| """ | |
| if not isinstance(s, str): | |
| raise TypeError('the JSON object must be str, not {!r}'.format( | |
| > s.__class__.__name__)) | |
| E TypeError: the JSON object must be str, not 'bytes' | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/json/__init__.py:312: TypeError | |
| --------------------------------------------------------------------- Captured stderr call --------------------------------------------------------------------- | |
| [NbConvertApp] Converting notebook /home/codeminer42/Code/open-source/aif360-learn/tests/../examples/demo_optim_data_preproc.ipynb to notebook | |
| [NbConvertApp] Executing notebook with kernel: python3 | |
| [NbConvertApp] Writing 56179 bytes to /tmp/tmpuatkjc6p.ipynb | |
| ______________________________________________________________ test_reject_option_classification _______________________________________________________________ | |
| def test_reject_option_classification(): | |
| nb, errors = notebook_run(os.path.join( | |
| os.path.dirname(os.path.abspath(__file__)), | |
| > '..', 'examples', 'demo_reject_option_classification.ipynb')) | |
| ../tests/test_demo_reject_option_classification.py:11: | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| ../tests/notebook_runner.py:33: in notebook_run | |
| nb = nbformat.read(fout, nbformat.current_nbformat) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:141: in read | |
| return reads(fp.read(), as_version, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:74: in reads | |
| nb = reader.reads(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:58: in reads | |
| nb_dict = parse_json(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:14: in parse_json | |
| nb_dict = json.loads(s, **kwargs) | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| s = b'{\n "cells": [\n {\n "cell_type": "markdown",\n "metadata": {},\n "source": [\n "#### This notebook demons...ython",\n "pygments_lexer": "ipython3",\n "version": "3.5.6"\n }\n },\n "nbformat": 4,\n "nbformat_minor": 2\n}\n' | |
| encoding = None, cls = None, object_hook = None, parse_float = None, parse_int = None, parse_constant = None, object_pairs_hook = None, kw = {} | |
| def loads(s, encoding=None, cls=None, object_hook=None, parse_float=None, | |
| parse_int=None, parse_constant=None, object_pairs_hook=None, **kw): | |
| """Deserialize ``s`` (a ``str`` instance containing a JSON | |
| document) to a Python object. | |
| ``object_hook`` is an optional function that will be called with the | |
| result of any object literal decode (a ``dict``). The return value of | |
| ``object_hook`` will be used instead of the ``dict``. This feature | |
| can be used to implement custom decoders (e.g. JSON-RPC class hinting). | |
| ``object_pairs_hook`` is an optional function that will be called with the | |
| result of any object literal decoded with an ordered list of pairs. The | |
| return value of ``object_pairs_hook`` will be used instead of the ``dict``. | |
| This feature can be used to implement custom decoders that rely on the | |
| order that the key and value pairs are decoded (for example, | |
| collections.OrderedDict will remember the order of insertion). If | |
| ``object_hook`` is also defined, the ``object_pairs_hook`` takes priority. | |
| ``parse_float``, if specified, will be called with the string | |
| of every JSON float to be decoded. By default this is equivalent to | |
| float(num_str). This can be used to use another datatype or parser | |
| for JSON floats (e.g. decimal.Decimal). | |
| ``parse_int``, if specified, will be called with the string | |
| of every JSON int to be decoded. By default this is equivalent to | |
| int(num_str). This can be used to use another datatype or parser | |
| for JSON integers (e.g. float). | |
| ``parse_constant``, if specified, will be called with one of the | |
| following strings: -Infinity, Infinity, NaN. | |
| This can be used to raise an exception if invalid JSON numbers | |
| are encountered. | |
| To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` | |
| kwarg; otherwise ``JSONDecoder`` is used. | |
| The ``encoding`` argument is ignored and deprecated. | |
| """ | |
| if not isinstance(s, str): | |
| raise TypeError('the JSON object must be str, not {!r}'.format( | |
| > s.__class__.__name__)) | |
| E TypeError: the JSON object must be str, not 'bytes' | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/json/__init__.py:312: TypeError | |
| --------------------------------------------------------------------- Captured stderr call --------------------------------------------------------------------- | |
| [NbConvertApp] Converting notebook /home/codeminer42/Code/open-source/aif360-learn/tests/../examples/demo_reject_option_classification.ipynb to notebook | |
| [NbConvertApp] Executing notebook with kernel: python3 | |
| [NbConvertApp] Writing 44535 bytes to /tmp/tmpi92b8pqo.ipynb | |
| ___________________________________________________________________ test_reweighing_preproc ____________________________________________________________________ | |
| def test_reweighing_preproc(): | |
| nb, errors = notebook_run(os.path.join( | |
| os.path.dirname(os.path.abspath(__file__)), | |
| > '..', 'examples', 'demo_reweighing_preproc.ipynb')) | |
| ../tests/test_demo_reweighing_preproc.py:11: | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| ../tests/notebook_runner.py:33: in notebook_run | |
| nb = nbformat.read(fout, nbformat.current_nbformat) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:141: in read | |
| return reads(fp.read(), as_version, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/__init__.py:74: in reads | |
| nb = reader.reads(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:58: in reads | |
| nb_dict = parse_json(s, **kwargs) | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/site-packages/nbformat/reader.py:14: in parse_json | |
| nb_dict = json.loads(s, **kwargs) | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| s = b'{\n "cells": [\n {\n "cell_type": "markdown",\n "metadata": {},\n "source": [\n "#### This notebook demons...ython",\n "pygments_lexer": "ipython3",\n "version": "3.5.6"\n }\n },\n "nbformat": 4,\n "nbformat_minor": 2\n}\n' | |
| encoding = None, cls = None, object_hook = None, parse_float = None, parse_int = None, parse_constant = None, object_pairs_hook = None, kw = {} | |
| def loads(s, encoding=None, cls=None, object_hook=None, parse_float=None, | |
| parse_int=None, parse_constant=None, object_pairs_hook=None, **kw): | |
| """Deserialize ``s`` (a ``str`` instance containing a JSON | |
| document) to a Python object. | |
| ``object_hook`` is an optional function that will be called with the | |
| result of any object literal decode (a ``dict``). The return value of | |
| ``object_hook`` will be used instead of the ``dict``. This feature | |
| can be used to implement custom decoders (e.g. JSON-RPC class hinting). | |
| ``object_pairs_hook`` is an optional function that will be called with the | |
| result of any object literal decoded with an ordered list of pairs. The | |
| return value of ``object_pairs_hook`` will be used instead of the ``dict``. | |
| This feature can be used to implement custom decoders that rely on the | |
| order that the key and value pairs are decoded (for example, | |
| collections.OrderedDict will remember the order of insertion). If | |
| ``object_hook`` is also defined, the ``object_pairs_hook`` takes priority. | |
| ``parse_float``, if specified, will be called with the string | |
| of every JSON float to be decoded. By default this is equivalent to | |
| float(num_str). This can be used to use another datatype or parser | |
| for JSON floats (e.g. decimal.Decimal). | |
| ``parse_int``, if specified, will be called with the string | |
| of every JSON int to be decoded. By default this is equivalent to | |
| int(num_str). This can be used to use another datatype or parser | |
| for JSON integers (e.g. float). | |
| ``parse_constant``, if specified, will be called with one of the | |
| following strings: -Infinity, Infinity, NaN. | |
| This can be used to raise an exception if invalid JSON numbers | |
| are encountered. | |
| To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` | |
| kwarg; otherwise ``JSONDecoder`` is used. | |
| The ``encoding`` argument is ignored and deprecated. | |
| """ | |
| if not isinstance(s, str): | |
| raise TypeError('the JSON object must be str, not {!r}'.format( | |
| > s.__class__.__name__)) | |
| E TypeError: the JSON object must be str, not 'bytes' | |
| ../../../../miniconda3/envs/aiflearn/lib/python3.5/json/__init__.py:312: TypeError | |
| --------------------------------------------------------------------- Captured stderr call --------------------------------------------------------------------- | |
| [NbConvertApp] Converting notebook /home/codeminer42/Code/open-source/aif360-learn/tests/../examples/demo_reweighing_preproc.ipynb to notebook | |
| [NbConvertApp] Executing notebook with kernel: python3 | |
| [NbConvertApp] Writing 57583 bytes to /tmp/tmp8xe484rd.ipynb | |
| _________________________________________________________________________ test_repair0 _________________________________________________________________________ | |
| def test_repair0(): | |
| ad = AdultDataset(protected_attribute_names=['sex'], | |
| privileged_classes=[['Male']], categorical_features=[], | |
| features_to_keep=['age', 'education-num']) | |
| > di = DisparateImpactRemover(repair_level=0.) | |
| ../tests/test_disparate_impact_remover.py:17: | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| self = <aiflearn.algorithms.preprocessing.disparate_impact_remover.DisparateImpactRemover object at 0x7ff158379438>, repair_level = 0.0 | |
| sensitive_attribute = None | |
| def __init__(self, repair_level=1.0, sensitive_attribute=None): | |
| super(DisparateImpactRemover, self).__init__(repair_level=repair_level) | |
| # avoid importing early since this package can throw warnings in some | |
| # jupyter notebooks | |
| > from BlackBoxAuditing.repairers.GeneralRepairer import Repairer | |
| E ImportError: No module named 'BlackBoxAuditing' | |
| ../aiflearn/algorithms/preprocessing/disparate_impact_remover.py:36: ImportError | |
| __________________________________________________________________________ test_adult __________________________________________________________________________ | |
| def test_adult(): | |
| protected = 'sex' | |
| ad = AdultDataset(protected_attribute_names=[protected], | |
| privileged_classes=[['Male']], categorical_features=[], | |
| features_to_keep=['age', 'education-num', 'capital-gain', | |
| 'capital-loss', 'hours-per-week']) | |
| scaler = MinMaxScaler(copy=False) | |
| # ad.features = scaler.fit_transform(ad.features) | |
| train, test = ad.split([32561]) | |
| assert np.any(test.labels) | |
| train.features = scaler.fit_transform(train.features) | |
| test.features = scaler.transform(test.features) | |
| index = train.feature_names.index(protected) | |
| X_tr = np.delete(train.features, index, axis=1) | |
| X_te = np.delete(test.features, index, axis=1) | |
| y_tr = train.labels.ravel() | |
| > di = DisparateImpactRemover(repair_level=1.0) | |
| ../tests/test_disparate_impact_remover.py:43: | |
| _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ | |
| self = <aiflearn.algorithms.preprocessing.disparate_impact_remover.DisparateImpactRemover object at 0x7ff15736a320>, repair_level = 1.0 | |
| sensitive_attribute = None | |
| def __init__(self, repair_level=1.0, sensitive_attribute=None): | |
| super(DisparateImpactRemover, self).__init__(repair_level=repair_level) | |
| # avoid importing early since this package can throw warnings in some | |
| # jupyter notebooks | |
| > from BlackBoxAuditing.repairers.GeneralRepairer import Repairer | |
| E ImportError: No module named 'BlackBoxAuditing' | |
| ../aiflearn/algorithms/preprocessing/disparate_impact_remover.py:36: ImportError | |
| ======================================================================= warnings summary ======================================================================= | |
| tests/test_lfr.py::test_fit_isnumpy | |
| /home/codeminer42/Code/open-source/aif360-learn/aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py:67: NumbaWarning: | |
| Compilation is falling back to object mode WITH looplifting enabled because Function "LFR_optim_obj" failed type inference due to: Unknown attribute 'iters' of type recursive(type(CPUDispatcher(<function LFR_optim_obj at 0x7ff133bdc510>))) | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 71: | |
| def LFR_optim_obj(params, data_sensitive, data_nonsensitive, y_sensitive, | |
| <source elided> | |
| LFR_optim_obj.iters += 1 | |
| ^ | |
| [1] During: typing of get attribute at /home/codeminer42/Code/open-source/aif360-learn/aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py (71) | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 71: | |
| def LFR_optim_obj(params, data_sensitive, data_nonsensitive, y_sensitive, | |
| <source elided> | |
| LFR_optim_obj.iters += 1 | |
| ^ | |
| @jit | |
| tests/test_lfr.py::test_fit_isnumpy | |
| /home/codeminer42/Code/open-source/aif360-learn/aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py:67: NumbaWarning: | |
| Compilation is falling back to object mode WITHOUT looplifting enabled because Function "LFR_optim_obj" failed type inference due to: cannot determine Numba type of <class 'numba.dispatcher.LiftedLoop'> | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 90: | |
| def LFR_optim_obj(params, data_sensitive, data_nonsensitive, y_sensitive, | |
| <source elided> | |
| L_z = 0.0 | |
| for j in range(k): | |
| ^ | |
| @jit | |
| tests/test_lfr.py::test_fit_isnumpy | |
| /home/codeminer42/miniconda3/envs/aiflearn/lib/python3.5/site-packages/numba/compiler.py:742: NumbaWarning: Function "LFR_optim_obj" was compiled in object mode without forceobj=True, but has lifted loops. | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 68: | |
| @jit | |
| def LFR_optim_obj(params, data_sensitive, data_nonsensitive, y_sensitive, | |
| ^ | |
| self.func_ir.loc)) | |
| tests/test_lfr.py::test_fit_isnumpy | |
| /home/codeminer42/miniconda3/envs/aiflearn/lib/python3.5/site-packages/numba/compiler.py:751: NumbaDeprecationWarning: | |
| Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. | |
| For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 68: | |
| @jit | |
| def LFR_optim_obj(params, data_sensitive, data_nonsensitive, y_sensitive, | |
| ^ | |
| warnings.warn(errors.NumbaDeprecationWarning(msg, self.func_ir.loc)) | |
| tests/test_lfr.py::test_transform_protecteddataset | |
| /home/codeminer42/Code/open-source/aif360-learn/aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py:55: NumbaWarning: | |
| Compilation is falling back to object mode WITH looplifting enabled because Function "yhat" failed type inference due to: Invalid use of Function(<built-in function iadd>) with argument(s) of type(s): (float64, array(float64, 1d, C)) | |
| Known signatures: | |
| * (int64, int64) -> int64 | |
| * (int64, uint64) -> int64 | |
| * (uint64, int64) -> int64 | |
| * (uint64, uint64) -> uint64 | |
| * (float32, float32) -> float32 | |
| * (float64, float64) -> float64 | |
| * (complex64, complex64) -> complex64 | |
| * (complex128, complex128) -> complex128 | |
| * parameterized | |
| In definition 0: | |
| All templates rejected with literals. | |
| In definition 1: | |
| All templates rejected without literals. | |
| In definition 2: | |
| All templates rejected with literals. | |
| In definition 3: | |
| All templates rejected without literals. | |
| In definition 4: | |
| All templates rejected with literals. | |
| In definition 5: | |
| All templates rejected without literals. | |
| In definition 6: | |
| All templates rejected with literals. | |
| In definition 7: | |
| All templates rejected without literals. | |
| In definition 8: | |
| All templates rejected with literals. | |
| In definition 9: | |
| All templates rejected without literals. | |
| In definition 10: | |
| All templates rejected with literals. | |
| In definition 11: | |
| All templates rejected without literals. | |
| In definition 12: | |
| All templates rejected with literals. | |
| In definition 13: | |
| All templates rejected without literals. | |
| This error is usually caused by passing an argument of a type that is unsupported by the named function. | |
| [1] During: typing of intrinsic-call at /home/codeminer42/Code/open-source/aif360-learn/aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py (64) | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 64: | |
| def yhat(M_nk, y, w, N, k): | |
| <source elided> | |
| yhat[i] = 0.999 if yhat[i] >= 1 else yhat[i] | |
| L_y += -1 * y[i] * np.log(yhat[i]) - (1.0 - y[i]) * np.log(1.0 - yhat[i]) | |
| ^ | |
| @jit | |
| tests/test_lfr.py::test_transform_protecteddataset | |
| /home/codeminer42/Code/open-source/aif360-learn/aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py:55: NumbaWarning: | |
| Compilation is falling back to object mode WITHOUT looplifting enabled because Function "yhat" failed type inference due to: cannot determine Numba type of <class 'numba.dispatcher.LiftedLoop'> | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 59: | |
| def yhat(M_nk, y, w, N, k): | |
| <source elided> | |
| L_y = 0.0 | |
| for i in range(N): | |
| ^ | |
| @jit | |
| tests/test_lfr.py::test_transform_protecteddataset | |
| /home/codeminer42/miniconda3/envs/aiflearn/lib/python3.5/site-packages/numba/compiler.py:742: NumbaWarning: Function "yhat" was compiled in object mode without forceobj=True, but has lifted loops. | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 56: | |
| @jit | |
| def yhat(M_nk, y, w, N, k): | |
| ^ | |
| self.func_ir.loc)) | |
| tests/test_lfr.py::test_transform_protecteddataset | |
| /home/codeminer42/miniconda3/envs/aiflearn/lib/python3.5/site-packages/numba/compiler.py:751: NumbaDeprecationWarning: | |
| Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. | |
| For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 56: | |
| @jit | |
| def yhat(M_nk, y, w, N, k): | |
| ^ | |
| warnings.warn(errors.NumbaDeprecationWarning(msg, self.func_ir.loc)) | |
| tests/test_lfr.py::test_transform_protecteddataset | |
| /home/codeminer42/Code/open-source/aif360-learn/aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py:55: NumbaWarning: | |
| Compilation is falling back to object mode WITHOUT looplifting enabled because Function "yhat" failed type inference due to: Invalid use of Function(<built-in function iadd>) with argument(s) of type(s): (float64, array(float64, 1d, C)) | |
| Known signatures: | |
| * (int64, int64) -> int64 | |
| * (int64, uint64) -> int64 | |
| * (uint64, int64) -> int64 | |
| * (uint64, uint64) -> uint64 | |
| * (float32, float32) -> float32 | |
| * (float64, float64) -> float64 | |
| * (complex64, complex64) -> complex64 | |
| * (complex128, complex128) -> complex128 | |
| * parameterized | |
| In definition 0: | |
| All templates rejected with literals. | |
| In definition 1: | |
| All templates rejected without literals. | |
| In definition 2: | |
| All templates rejected with literals. | |
| In definition 3: | |
| All templates rejected without literals. | |
| In definition 4: | |
| All templates rejected with literals. | |
| In definition 5: | |
| All templates rejected without literals. | |
| In definition 6: | |
| All templates rejected with literals. | |
| In definition 7: | |
| All templates rejected without literals. | |
| In definition 8: | |
| All templates rejected with literals. | |
| In definition 9: | |
| All templates rejected without literals. | |
| In definition 10: | |
| All templates rejected with literals. | |
| In definition 11: | |
| All templates rejected without literals. | |
| In definition 12: | |
| All templates rejected with literals. | |
| In definition 13: | |
| All templates rejected without literals. | |
| This error is usually caused by passing an argument of a type that is unsupported by the named function. | |
| [1] During: typing of intrinsic-call at /home/codeminer42/Code/open-source/aif360-learn/aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py (64) | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 64: | |
| def yhat(M_nk, y, w, N, k): | |
| <source elided> | |
| yhat[i] = 0.999 if yhat[i] >= 1 else yhat[i] | |
| L_y += -1 * y[i] * np.log(yhat[i]) - (1.0 - y[i]) * np.log(1.0 - yhat[i]) | |
| ^ | |
| @jit | |
| tests/test_lfr.py::test_transform_protecteddataset | |
| /home/codeminer42/miniconda3/envs/aiflearn/lib/python3.5/site-packages/numba/compiler.py:742: NumbaWarning: Function "yhat" was compiled in object mode without forceobj=True. | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 59: | |
| def yhat(M_nk, y, w, N, k): | |
| <source elided> | |
| L_y = 0.0 | |
| for i in range(N): | |
| ^ | |
| self.func_ir.loc)) | |
| tests/test_lfr.py::test_transform_protecteddataset | |
| /home/codeminer42/miniconda3/envs/aiflearn/lib/python3.5/site-packages/numba/compiler.py:751: NumbaDeprecationWarning: | |
| Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour. | |
| For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit | |
| File "../aiflearn/algorithms/preprocessing/lfr_helpers/helpers.py", line 59: | |
| def yhat(M_nk, y, w, N, k): | |
| <source elided> | |
| L_y = 0.0 | |
| for i in range(N): | |
| ^ | |
| warnings.warn(errors.NumbaDeprecationWarning(msg, self.func_ir.loc)) | |
| -- Docs: https://docs.pytest.org/en/latest/warnings.html | |
| ==================================================== 8 failed, 29 passed, 11 warnings in 906.57s (0:15:06) ===================================================== | |
| RUNNING THE L-BFGS-B CODE | |
| * * * | |
| Machine precision = 2.220D-16 | |
| N = 131 M = 10 | |
| At X0 0 variables are exactly at the bounds | |
| At iterate 0 f= 2.26526D+04 |proj g|= 1.58260D+03 | |
| At iterate 1 f= 2.04189D+04 |proj g|= 4.91265D+02 | |
| At iterate 2 f= 2.01309D+04 |proj g|= 4.96682D+02 | |
| At iterate 3 f= 1.97260D+04 |proj g|= 4.65151D+02 | |
| At iterate 4 f= 1.90393D+04 |proj g|= 8.07770D+02 | |
| At iterate 5 f= 1.88301D+04 |proj g|= 7.56099D+02 | |
| At iterate 6 f= 1.87428D+04 |proj g|= 8.94691D+02 | |
| At iterate 7 f= 1.83953D+04 |proj g|= 8.81409D+02 | |
| At iterate 8 f= 1.75778D+04 |proj g|= 1.46632D+03 | |
| At iterate 9 f= 1.73528D+04 |proj g|= 8.93699D+02 | |
| At iterate 10 f= 1.72061D+04 |proj g|= 9.64026D+02 | |
| At iterate 11 f= 1.68696D+04 |proj g|= 9.75129D+02 | |
| At iterate 12 f= 1.66957D+04 |proj g|= 6.94447D+02 | |
| At iterate 13 f= 1.64707D+04 |proj g|= 8.57689D+02 | |
| At iterate 14 f= 1.61611D+04 |proj g|= 3.70558D+02 | |
| At iterate 15 f= 1.60457D+04 |proj g|= 5.00958D+02 | |
| At iterate 16 f= 1.59239D+04 |proj g|= 4.47909D+02 | |
| At iterate 17 f= 1.58222D+04 |proj g|= 9.56907D+02 | |
| At iterate 18 f= 1.57106D+04 |proj g|= 6.13336D+02 | |
| At iterate 19 f= 1.56562D+04 |proj g|= 3.55303D+02 | |
| At iterate 20 f= 1.55893D+04 |proj g|= 3.50948D+02 | |
| At iterate 21 f= 1.55119D+04 |proj g|= 2.49244D+02 | |
| At iterate 22 f= 1.54403D+04 |proj g|= 2.58441D+02 | |
| At iterate 23 f= 1.53811D+04 |proj g|= 2.76940D+02 | |
| At iterate 24 f= 1.53437D+04 |proj g|= 5.52288D+02 | |
| At iterate 25 f= 1.52968D+04 |proj g|= 2.03731D+02 | |
| At iterate 26 f= 1.52443D+04 |proj g|= 2.27741D+02 | |
| At iterate 27 f= 1.52216D+04 |proj g|= 2.62413D+02 | |
| At iterate 28 f= 1.51943D+04 |proj g|= 2.26614D+02 | |
| At iterate 29 f= 1.51479D+04 |proj g|= 1.08968D+02 | |
| At iterate 30 f= 1.51290D+04 |proj g|= 1.41082D+02 | |
| At iterate 31 f= 1.50983D+04 |proj g|= 1.44776D+02 | |
| At iterate 32 f= 1.50906D+04 |proj g|= 1.54591D+02 | |
| * * * | |
| Tit = total number of iterations | |
| Tnf = total number of function evaluations | |
| Tnint = total number of segments explored during Cauchy searches | |
| Skip = number of BFGS updates skipped | |
| Nact = number of active bounds at final generalized Cauchy point | |
| Projg = norm of the final projected gradient | |
| F = final function value | |
| * * * | |
| N Tit Tnf Tnint Skip Nact Projg F | |
| 131 32 38 41 0 3 1.546D+02 1.509D+04 | |
| F = 15090.552840313529 | |
| STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT | |
| Cauchy time 0.000E+00 seconds. | |
| Subspace minimization time 0.000E+00 seconds. | |
| Line search time 0.000E+00 seconds. | |
| Total User time 0.000E+00 seconds. |
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