It's probably because I built it in a different way.
Here is the steps for how I built it:
1. Type arch -32
/System/Library/Frameworks/Python.framework/Versions/2.7/bin/python -c
"import sys; print sys.maxint" and make sure it outputs 2147483647.
2. Modify line 5 of Makefile exists in root directory of scikit-learn
become:
PYTHON ?= arch -32
/System/Library/Frameworks/Python.framework/Versions/2.7/bin/python
and modify line 11 to:
BITS := $(shell PYTHON -c 'import struct; print(8 *
struct.calcsize("P"))')
3. Type sudo make in the root directory of scikit-learn to build a 32
bit version.
It reports OK and no test failures after sudo make complete.
BTW, then may you please run this branch
<https://github.com/yenchenlin/scikit-learn/tree/cd-fused-types> and see if
there are any errors relate to enet?
Thanks a lot for helping me.
Best,
Yen-Chen
2016-07-21 0:55 GMT+08:00 Matthew Brett <[email protected]>:
> On Wed, Jul 20, 2016 at 5:25 PM, lin yenchen <[email protected]>
> wrote:
> > Thanks for you guys' precious inputs.
> >
> > I've successfully built a 32-bit python version scikit-learn and check
> it by
> > printing `sys.maxint`,
> > and all the tests passed on my mac. (I'm running the newest dev version
> > though)
>
> On current master I get the following failures from:
>
> arch -i386 nosetests sklearn
>
>
> ```
> ======================================================================
> ERROR:
> sklearn.decomposition.tests.test_nmf.test_non_negative_factorization_checking
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File
> "/Users/mb312/.virtualenvs/test/lib/python2.7/site-packages/nose/case.py",
> line 197, in runTest
> self.test(*self.arg)
> File "/Users/mb312/dev_trees/scikit-learn/sklearn/utils/testing.py",
> line 342, in wrapper
> return fn(*args, **kwargs)
> File
> "/Users/mb312/dev_trees/scikit-learn/sklearn/decomposition/tests/test_nmf.py",
> line 237, in test_non_negative_factorization_checking
> assert_no_warnings(nnmf, A, A, A, np.int64(1))
> File "/Users/mb312/dev_trees/scikit-learn/sklearn/utils/testing.py",
> line 272, in assert_no_warnings
> result = func(*args, **kw)
> File "/Users/mb312/dev_trees/scikit-learn/sklearn/decomposition/nmf.py",
> line 751, in non_negative_factorization
> " got (n_components=%r)" % n_components)
> ValueError: Number of components must be a positive integer; got
> (n_components=1)
>
> ======================================================================
> ERROR: Test that it gives proper exception on deficient input.
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File
> "/Users/mb312/.virtualenvs/test/lib/python2.7/site-packages/nose/case.py",
> line 197, in runTest
> self.test(*self.arg)
> File
> "/Users/mb312/dev_trees/scikit-learn/sklearn/ensemble/tests/test_iforest.py",
> line 107, in test_iforest_error
> assert_no_warnings(IsolationForest(max_samples=np.int64(2)).fit, X)
> File "/Users/mb312/dev_trees/scikit-learn/sklearn/utils/testing.py",
> line 272, in assert_no_warnings
> result = func(*args, **kw)
> File "/Users/mb312/dev_trees/scikit-learn/sklearn/ensemble/iforest.py",
> line 182, in fit
> raise ValueError("max_samples must be in (0, 1]")
> ValueError: max_samples must be in (0, 1]
>
> ======================================================================
> ERROR: sklearn.linear_model.tests.test_huber.test_huber_better_r2_score
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File
> "/Users/mb312/.virtualenvs/test/lib/python2.7/site-packages/nose/case.py",
> line 197, in runTest
> self.test(*self.arg)
> File
> "/Users/mb312/dev_trees/scikit-learn/sklearn/linear_model/tests/test_huber.py",
> line 170, in test_huber_better_r2_score
> huber_score = huber.score(X[mask], y[mask])
> File "/Users/mb312/dev_trees/scikit-learn/sklearn/base.py", line 363, in
> score
> return r2_score(y, self.predict(X), sample_weight=sample_weight,
> File "/Users/mb312/dev_trees/scikit-learn/sklearn/linear_model/base.py",
> line 268, in predict
> return self._decision_function(X)
> File "/Users/mb312/dev_trees/scikit-learn/sklearn/linear_model/base.py",
> line 251, in _decision_function
> X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
> File "/Users/mb312/dev_trees/scikit-learn/sklearn/utils/validation.py",
> line 415, in check_array
> context))
> ValueError: Found array with 0 sample(s) (shape=(0, 20)) while a
> minimum of 1 is required.
>
> ======================================================================
> ERROR: sklearn.tree.tests.test_tree.test_huge_allocations
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File
> "/Users/mb312/.virtualenvs/test/lib/python2.7/site-packages/nose/case.py",
> line 197, in runTest
> self.test(*self.arg)
> File
> "/Users/mb312/dev_trees/scikit-learn/sklearn/tree/tests/test_tree.py",
> line 1089, in test_huge_allocations
> assert_raises(MemoryError, clf.fit, X, y)
> File
> "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/unittest/case.py",
> line 473, in assertRaises
> callableObj(*args, **kwargs)
> File "/Users/mb312/dev_trees/scikit-learn/sklearn/tree/tree.py",
> line 366, in fit
> max_leaf_nodes)
> File "sklearn/tree/_tree.pyx", line 292, in
> sklearn.tree._tree.BestFirstTreeBuilder.__cinit__
> (sklearn/tree/_tree.c:4728)
> SIZE_t max_depth, SIZE_t max_leaf_nodes):
> OverflowError: Python int too large to convert to C long
>
> ======================================================================
> FAIL: Test that outliers filtering is scaling independent.
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File
> "/Users/mb312/.virtualenvs/test/lib/python2.7/site-packages/nose/case.py",
> line 197, in runTest
> self.test(*self.arg)
> File
> "/Users/mb312/dev_trees/scikit-learn/sklearn/linear_model/tests/test_huber.py",
> line 120, in test_huber_scaling_invariant
> assert_array_equal(n_outliers_mask_3, n_outliers_mask_1)
> File
> "/Users/mb312/.virtualenvs/test/lib/python2.7/site-packages/numpy/testing/utils.py",
> line 719, in assert_array_equal
> verbose=verbose, header='Arrays are not equal')
> File
> "/Users/mb312/.virtualenvs/test/lib/python2.7/site-packages/numpy/testing/utils.py",
> line 645, in assert_array_compare
> raise AssertionError(msg)
> AssertionError:
> Arrays are not equal
>
> (mismatch 66.0%)
> x: array([ True, False, False, True, False, False, False, False, False,
> False, False, False, False, False, False, False, True, True,
> False, False, False, True, True, False, True, True, False,...
> y: array([ True, True, True, True, True, True, True, True, True,
> True, True, True, True, True, True, True, True, True,
> True, True, True, True, True, True, True, True, True,...
>
> ======================================================================
> FAIL: Test they should converge to same coefficients for same parameters
> ----------------------------------------------------------------------
> Traceback (most recent call last):
> File
> "/Users/mb312/.virtualenvs/test/lib/python2.7/site-packages/nose/case.py",
> line 197, in runTest
> self.test(*self.arg)
> File
> "/Users/mb312/dev_trees/scikit-learn/sklearn/linear_model/tests/test_huber.py",
> line 136, in test_huber_and_sgd_same_results
> assert_almost_equal(huber.scale_, 1.0, 3)
> File
> "/Users/mb312/.virtualenvs/test/lib/python2.7/site-packages/numpy/testing/utils.py",
> line 468, in assert_almost_equal
> raise AssertionError(msg)
> AssertionError:
> Arrays are not almost equal to 3 decimals
> ACTUAL: 3.6103567932800094e-11
> DESIRED: 1.0
> ```
>
> I wonder why our results are different?
>
> Cheers,
>
> Matthew
> _______________________________________________
> scikit-learn mailing list
> [email protected]
> https://mail.python.org/mailman/listinfo/scikit-learn
>
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