Hey all.
A slightly off-topic question about cython profiling.
I'm pretty sure I could use yep for profiling, as mentioned in the docs:
http://scikit-learn.org/dev/developers/performance.html#profiling-compiled-extensions
and get line-by-line counts.
However I did not manage to do that recently.
2014-07-16 16:43 GMT+02:00 Andy t3k...@gmail.com:
I'm pretty sure I could use yep for profiling, as mentioned in the docs:
http://scikit-learn.org/dev/developers/performance.html#profiling-compiled-extensions
and get line-by-line counts.
However I did not manage to do that recently. I ususally
On 07/16/2014 05:17 PM, Lars Buitinck wrote:
2014-07-16 16:43 GMT+02:00 Andy t3k...@gmail.com:
I'm pretty sure I could use yep for profiling, as mentioned in the docs:
http://scikit-learn.org/dev/developers/performance.html#profiling-compiled-extensions
and get line-by-line counts.
However I
2014-07-16 17:29 GMT+02:00 Andy t3k...@gmail.com:
That is using google perftools.
I thought you were referring to the bit about gprof.
So you get line-by-line with google perftools without using debugging
versions? How?
I don't, I look at per-function cost.
now way to really get line by line without some sort of debug afaik, it
throws the info away at compile time
On Wed, Jul 16, 2014 at 11:44 AM, Lars Buitinck larsm...@gmail.com wrote:
2014-07-16 17:29 GMT+02:00 Andy t3k...@gmail.com:
That is using google perftools.
I thought you were
Hi,
I have noticed a change with the LabelBinarizer between version 0.15
and those before.
Prior 0.15, this worked:
lb = LabelBinarizer()
lb.fit_transform(['a', 'b', 'c'])
array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
lb.transform(['a', 'd', 'e'])
array([[1, 0, 0],
[0, 0, 0],
Hi
This looks like a regression. Can you open an issue on github?
I am not sure that it would make sense to add a unknown columns
label with an optional parameter. But you could easily add one with
some numpy operations
np.hstack([y, y.sum(axis=1,keepdims=True) == 0])
Best regards,
Arnaud
I can open an issue, but on the other hand, you could argue that the
new behaviour is now at least consistent with the other encoder types,
e.g.:
le = LabelEncoder()
le.fit_transform(['a', 'b', 'c'])
array([0, 1, 2])
le.transform(['a', 'd', 'e'])
[...]
ValueError: y contains new labels: ['d'
cf. https://github.com/scikit-learn/scikit-learn/pull/3243
On 17 July 2014 08:59, Christian Jauvin cjau...@gmail.com wrote:
I can open an issue, but on the other hand, you could argue that the
new behaviour is now at least consistent with the other encoder types,
e.g.:
le = LabelEncoder()
Relevant to this:
https://github.com/scikit-learn/scikit-learn/pull/3243
Thanks,
Michael J. Bommarito II, CEO
Bommarito Consulting, LLC
*Web:* http://www.bommaritollc.com
*Mobile:* +1 (646) 450-3387
On Wed, Jul 16, 2014 at 6:59 PM, Christian Jauvin cjau...@gmail.com wrote:
I can open an
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