add sklearn version to external version in sklearn flows,

[amueller]

closes#734.

[mfeurer]

Yep, that should solve it, thanks for the PR. However, it seems like you forgot to add the MyLR class.

[amueller]

damn, should be MyDummy now... I'll fix after lunch.

[amueller]

Do you want me to remove this? Would work without it but I found it more explicit to directly give the extension.

[mfeurer]

This is totally fine by me.

[amueller]

probably fine now ;)

[mfeurer]

Thanks, this looks good now. However, I'm afraid that your unit test fails for scikit-learn 0.18.X:

self = <tests.test_extensions.test_sklearn_extension.test_sklearn_extension.TestSklearnExtensionRunFunctions testMethod=test_run_model_on_task>
def test_run_model_on_task(self):
class MyDummy(sklearn.dummy.DummyClassifier):
pass
task = openml.tasks.get_task(1)
> openml.runs.run_model_on_task(MyDummy(), task)
/home/travis/build/openml/openml-python/tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py:1226: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ /home/travis/build/openml/openml-python/openml/runs/functions.py:106: in run_model_on_task
upload_flow=upload_flow,
/home/travis/build/openml/openml-python/openml/runs/functions.py:222: in run_flow_on_task
add_local_measures=add_local_measures,
/home/travis/build/openml/openml-python/openml/runs/functions.py:446: in _run_task_get_arffcontent
X_test=test_x,
/home/travis/build/openml/openml-python/openml/extensions/sklearn/extension.py:1248: in _run_model_on_fold
pred_y = model_copy.predict(X_test)
/home/travis/miniconda/envs/testenv/lib/python3.6/site-packages/sklearn/dummy.py:174: in predict
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
/home/travis/miniconda/envs/testenv/lib/python3.6/site-packages/sklearn/utils/validation.py:407: in check_array
_assert_all_finite(array)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ X = array([[nan, 0., 1., ..., 0., 0., nan],
[ 8., 0., 1., ..., nan, 0., nan],
[ 8., 0., 1., ..., na...nan, 0., nan],
[nan, 0., nan, ..., nan, 0., nan],
[ 8., 0., 0., ..., nan, 0., nan]], dtype=float32)
def _assert_all_finite(X):
"""Like assert_all_finite, but only for ndarray."""
X = np.asanyarray(X)
# First try an O(n) time, O(1) space solution for the common case that
# everything is finite; fall back to O(n) space np.isfinite to prevent
# false positives from overflow in sum method.
if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
and not np.isfinite(X).all()):
raise ValueError("Input contains NaN, infinity"
> " or a value too large for %r." % X.dtype)
E ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
/home/travis/miniconda/envs/testenv/lib/python3.6/site-packages/sklearn/utils/validation.py:58: ValueError

Could you please check that?

[amueller]

I think the failures now are unrelated?

[amueller]

ping @PGijsbers ;)
This one is what prevents us from pushing flows with non-sklearn components