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upgrade travis to run on new numpy release #9096
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It fails but not for the reason you wanted ;-):
|
Should be rendered now. |
vene
reviewed
Jun 11, 2017
.travis.yml
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@@ -38,17 +38,17 @@ matrix: | |||
# This environment tests the newest supported Anaconda release (4.4.0) | |||
# It also runs tests requiring Pandas. | |||
- env: DISTRIB="conda" PYTHON_VERSION="3.6.1" INSTALL_MKL="true" | |||
NUMPY_VERSION="1.12.1" SCIPY_VERSION="0.19.0" PANDAS_VERSION="0.20.1" | |||
NUMPY_VERSION="1.13" SCIPY_VERSION="0.19.0" PANDAS_VERSION="0.20.1" |
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bumping pandas to 0.20.2 should do it, according to this. (don't forget the commented out env below)
# Conflicts: # .travis.yml
Well, tests pass... Merge? |
I'd say so? |
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remove outdated comment fix also for FeatureUnion [MRG+2] Limiting n_components by both n_features and n_samples instead of just n_features (Recreated PR) (scikit-learn#8742) [MRG+1] Remove hard dependency on nose (scikit-learn#9670) MAINT Stop vendoring sphinx-gallery (scikit-learn#9403) CI upgrade travis to run on new numpy release (scikit-learn#9096) CI Make it possible to run doctests in .rst files with pytest (scikit-learn#9697) * doc/datasets/conftest.py to implement the equivalent of nose fixtures * add conftest.py in root folder to ensure that sklearn local folder is used rather than the package in site-packages * test doc with pytest in Travis * move custom_data_home definition from nose fixture to .rst file [MRG+1] avoid integer overflow by using floats for matthews_corrcoef (scikit-learn#9693) * Fix bug#9622: avoid integer overflow by using floats for matthews_corrcoef * matthews_corrcoef: cosmetic change requested by jnothman * Add test_matthews_corrcoef_overflow for Bug#9622 * test_matthews_corrcoef_overflow: clean-up and make deterministic * matthews_corrcoef: pass dtype=np.float64 to sum & trace instead of using astype * test_matthews_corrcoef_overflow: add simple deterministic tests TST Platform independent hash collision tests in FeatureHasher (scikit-learn#9710) TST More informative error message in test_preserve_trustworthiness_approximately (scikit-learn#9738) add some rudimentary tests for meta-estimators fix extra whitespace in error message add missing if_delegate_has_method in pipeline don't test tuple pipeline for now only copy list if not list already? doesn't seem to help?
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might break?