Just came across this one today:
np.in1d([1], set([0, 1, 2]), assume_unique=True)
array([ False], dtype=bool)
np.in1d([1], [0, 1, 2], assume_unique=True)
array([ True], dtype=bool)
I am assuming this has something to do with the fact that order is not guaranteed with set() objects? I was kind of hoping that setting "assume_unique=True" would be sufficient to overcome that problem. Should sets be rejected as an error?
This was using v1.9.0
Cheers! Ben Root
On Mo, 2015-08-10 at 12:09 -0400, Benjamin Root wrote:
Just came across this one today:
np.in1d([1], set([0, 1, 2]), assume_unique=True)
array([ False], dtype=bool)
np.in1d([1], [0, 1, 2], assume_unique=True)
array([ True], dtype=bool)
I am assuming this has something to do with the fact that order is not guaranteed with set() objects? I was kind of hoping that setting "assume_unique=True" would be sufficient to overcome that problem. Should sets be rejected as an error?
Not really, it is "simply" because ``np.asarray(set([1, 2, 3]))`` returns an object array and 1 is not the same as ``set([1, 2, 3])``.
I think earlier numpy versions may have had "short cuts" for short lists or something so this may have worked in some cases....
- Sebastian
This was using v1.9.0
Cheers!
Ben Root
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Another case where refusing to implicitly create object arrays would have avoided a lot of confusion... On Aug 10, 2015 10:13 AM, "Sebastian Berg" sebastian@sipsolutions.net wrote:
On Mo, 2015-08-10 at 12:09 -0400, Benjamin Root wrote:
Just came across this one today:
np.in1d([1], set([0, 1, 2]), assume_unique=True)
array([ False], dtype=bool)
np.in1d([1], [0, 1, 2], assume_unique=True)
array([ True], dtype=bool)
I am assuming this has something to do with the fact that order is not guaranteed with set() objects? I was kind of hoping that setting "assume_unique=True" would be sufficient to overcome that problem. Should sets be rejected as an error?
Not really, it is "simply" because ``np.asarray(set([1, 2, 3]))`` returns an object array and 1 is not the same as ``set([1, 2, 3])``.
I think earlier numpy versions may have had "short cuts" for short lists or something so this may have worked in some cases....
- Sebastian
This was using v1.9.0
Cheers!
Ben Root
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Not really, it is "simply" because ``np.asarray(set([1, 2, 3]))`` returns an object array
Holy crap! To be pedantic, it looks like it turns it into a numpy scalar, but still! I wouldn't have expected np.asarray() on a set (or dictionary, for that matter) to work because order is not guaranteed. Is this expected behavior?
Digging into the implementation of in1d(), I can see now how passing a set() wouldn't be useful at all (as an aside, pretty clever algorithm). I know sets aren't array-like, but the code that used this seemed to work at first, and this problem wasn't revealed until I created some unit tests to exercise some possible corner cases. Silently producing possibly erroneous results is dangerous. Don't know if better documentation or some better sanity checking would be called for here, though.
Ben Root
On Mon, Aug 10, 2015 at 1:10 PM, Sebastian Berg sebastian@sipsolutions.net wrote:
On Mo, 2015-08-10 at 12:09 -0400, Benjamin Root wrote:
Just came across this one today:
np.in1d([1], set([0, 1, 2]), assume_unique=True)
array([ False], dtype=bool)
np.in1d([1], [0, 1, 2], assume_unique=True)
array([ True], dtype=bool)
I am assuming this has something to do with the fact that order is not guaranteed with set() objects? I was kind of hoping that setting "assume_unique=True" would be sufficient to overcome that problem. Should sets be rejected as an error?
Not really, it is "simply" because ``np.asarray(set([1, 2, 3]))`` returns an object array and 1 is not the same as ``set([1, 2, 3])``.
I think earlier numpy versions may have had "short cuts" for short lists or something so this may have worked in some cases....
- Sebastian
This was using v1.9.0
Cheers!
Ben Root
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Mon, Aug 10, 2015 at 1:40 PM, Benjamin Root ben.root@ou.edu wrote:
Not really, it is "simply" because ``np.asarray(set([1, 2, 3]))`` returns an object array
Holy crap! To be pedantic, it looks like it turns it into a numpy scalar, but still! I wouldn't have expected np.asarray() on a set (or dictionary, for that matter) to work because order is not guaranteed. Is this expected behavior?
Digging into the implementation of in1d(), I can see now how passing a set() wouldn't be useful at all (as an aside, pretty clever algorithm). I know sets aren't array-like, but the code that used this seemed to work at first, and this problem wasn't revealed until I created some unit tests to exercise some possible corner cases. Silently producing possibly erroneous results is dangerous. Don't know if better documentation or some better sanity checking would be called for here, though.
Ben Root
On Mon, Aug 10, 2015 at 1:10 PM, Sebastian Berg < sebastian@sipsolutions.net> wrote:
On Mo, 2015-08-10 at 12:09 -0400, Benjamin Root wrote:
Just came across this one today:
np.in1d([1], set([0, 1, 2]), assume_unique=True)
array([ False], dtype=bool)
np.in1d([1], [0, 1, 2], assume_unique=True)
array([ True], dtype=bool)
I am assuming this has something to do with the fact that order is not guaranteed with set() objects? I was kind of hoping that setting "assume_unique=True" would be sufficient to overcome that problem. Should sets be rejected as an error?
Not really, it is "simply" because ``np.asarray(set([1, 2, 3]))`` returns an object array and 1 is not the same as ``set([1, 2, 3])``.
I think earlier numpy versions may have had "short cuts" for short lists or something so this may have worked in some cases....
is it possible to get at least a UserWarning when creating an object array and dtype object hasn't been explicitly requested or underlying data is already in an object dtype?
Josef
- Sebastian
This was using v1.9.0
Cheers!
Ben Root
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion