Mailman 3 October 2016 - scikit-learn

Recurrent States with Decision Tree
by KevNo Oct. 22, 2016

Oct. 22, 2016

Hello, Just wondering, how can we setup path dependant input states for Random Forest/Decision Tree ? This is similar to Recurrent Network, where input Xt=(x0,...,xi,.. yt-1, yt-2) depends on past output states Yt. If we could put the exact values states Yt, it obviously creates a bias in the training. So, we should be put some estimate of (Yt) Is the concept of Recurrent Tree makes sense ? Thanks for your insight. > scikit-learn-request(a)python.org <mailto:scikit-learn-request@python.org> > Thursday, October 20, 2016 1:00 AM > Send scikit-learn mailing list submissions to > scikit-learn(a)python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/scikit-learn > or, via email, send a message with subject or body 'help' to > scikit-learn-request(a)python.org > > You can reach the person managing the list at > scikit-learn-owner(a)python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of scikit-learn digest..." > > > Today's Topics: > > 1. Towards 0.18.1 (Joel Nothman) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Thu, 20 Oct 2016 01:42:31 +1100 > From: Joel Nothman <joel.nothman(a)gmail.com> > To: Scikit-learn user and developer mailing list > <scikit-learn(a)python.org> > Subject: [scikit-learn] Towards 0.18.1 > Message-ID: > <CAAkaFLWLtz8O02vVLweWEZwM_yVDV2M6OQN6yd30_B0U1MSEgg(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > Due to a few substantial bugs in 0.18.0, we're hoping to release 0.18.1 > around the end of the month. Help solving (and reviewing) the issues > listed > https://github.com/scikit-learn/scikit-learn/milestone/22 is welcome. In > particular, an easy documentation issue at > https://github.com/scikit-learn/scikit-learn/pull/7659 is waiting to be > picked up. >

1 0

Towards 0.18.1
by Joel Nothman Oct. 19, 2016

Oct. 19, 2016

Due to a few substantial bugs in 0.18.0, we're hoping to release 0.18.1 around the end of the month. Help solving (and reviewing) the issues listed https://github.com/scikit-learn/scikit-learn/milestone/22 is welcome. In particular, an easy documentation issue at https://github.com/scikit-learn/scikit-learn/pull/7659 is waiting to be picked up.

1 0

Silhouette example - performance issue
by Anaël Bonneton Oct. 18, 2016

Oct. 18, 2016

Hi, In the silhouette example ( http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette…), the silhouette values of each sample is computed twice: once with *silhouette_score *and once with *silhouette_samples.* The call to *silhouette_score* can be easily avoided by computing the average of the result of* silhouette_samples*. Do you think we should remove the call to *silhouette_score* to improve the performance ? Or it is better to keep the two functions to show how to use them ? Anaël Bonneton

4 3

Search in results for optimal parameter subsets
by Tomas Karasek Oct. 17, 2016

Oct. 17, 2016

Hey, I have dataframe with test results (10k rows). Each result (row) has ~6 parameters, plus some output metrics. I would like to find combinations of the parameters which have reasonable mean, std and support-count (number of results in the configuration). E.g. if there are parameters "k" and "n", each in range(100), and the result metric has good mean and std for "k in [4..12] and c in [90..95]" (support-count for this would be 8*5 = 40) and then maybe "k in [34..41] and c in [10..13] (s-c is 7*3=21), then I would like to have the algorithm return sth like k c mean std support-count total_score 4..12 90..95 12.1 1.23 40 9.3 34..41 10..13 11.1 1.13 21 6.2 I understand I will first have to define a fucntion that will reduce the mean, std and count to the total_score. I can do that somehow. But I don't know what kind of math task is finding the local maxima of parameter configuration subsets. Is this optimization task? Can you please point me to sth in sklearn or scipy, that would give me some direction? Cheers, Tomas

1 0

Missing data and decision trees
by Stuart Reynolds Oct. 13, 2016

Oct. 13, 2016

I'm looking for a decision tree and RF implementation that supports missing data (without imputation) -- ideally in Python, Java/Scala or C++. It seems that scikit's decision tree algorithm doesn't allow this -- which is disappointing because its one of the few methods that should be able to sensibly handle problems with high amounts of missingness. Are there plans to allow missing data in scikit's decision trees? Also, is there any particular reason why missing values weren't supported originally (e.g. integrates poorly with other features) Regards - Stuart

7 6

Re: [scikit-learn] Permission for creating new labels
by Andreas Mueller Oct. 13, 2016

Oct. 13, 2016

going to the mailing list On 10/13/2016 01:35 AM, Raghav R V wrote: > Thanks for the messages {Ga|Jo}el. ;) > > > We can use "needs second review" as an alternative to "MRG+1" but I > don't see the point of using both. > > I see the system of MRG+1 and MRG+2 as a more robust way of tracking > approvals to see if the PR can be merged (I'm not sure if review > approvals completely replace this?) and "Needs 2nd Review" as a quick > way to search... "Needs 2nd Review" could also be used with MRG PRs > which have already received a solid review and would need a 2nd look > from those who don't have much time to do a full fledged review... > > >By the way: this discussion should happen on the ML. > > Sorry for that. I wasn't sure if this was a very useful/non-trivial > suggestion and wanted to avoid noise there... > > > "Needs triage": > > I see that we have "Stale" label for that. > I just added this to make it easier to find PRs to review. I'm not sure if it is not redundant with the "needs contrib" tag on a PR. I used "stale" if I was not sure if it's worth working on something and the author didn't respond for a while. I haven't used it a lot yet. I'm ambivalent about adding a "approved by one" (which I think is more explicit then "need one more") tag. You can search for PRs and issues without comments - I recently did that to make sure everything had at least one ;) I'm not sure you can search for the absence of tags. But I am planning to go through all issues tomorrow to see stuff that I have missed. I'll be catching up today with all notifications that I missed this year because writing my ;) Maybe having an list of statuses for PRs and issues that covers the common cases would be good, we just kind of had that discussion, right? Issues can be bug|enhancement|new feature with status needs contributor, has PR or needs confirmation/discussion. It would be nice to see if a issue has a PR, I think there is no way to do that from the search. PRs need changes or reviews or are stalled (which is "needs changes" for a long time and no response) and then might "need contributor". We could use "needs review" on issues and add a "has PR" tag for issues and a "one approval" tag for PRs. I agree with Joel that switching between "needs review" and "needs changes" in a currently active PR is likely to be cumbersome.

2 1

Doubt regarding issue timeline
by Siddharth Gupta Oct. 12, 2016

Oct. 12, 2016

Hello fellas, I have a doubt. Suppose I ask to volunteer in working on an issue but due to some unavoidable scenario I fail to work on it for sometime, when should I let the community know about the same. I guess it depends on the issue/bug, but on an average how much time should one take to resolve an issue. Regards Siddharth Gupta, Ph: 9871012292 Linkedin <https://www.linkedin.com/in/sidgupta234/> | Github <https://github.com/sidgupta234> | Codechef <https://www.codechef.com/users/sidgupta234> | Twitter <https://twitter.com/SidGupta234> | Facebook <https://www.facebook.com/profile.php?id=1483695876>

3 2

HashingVectorizer slow in version 0.18
by Gabriel Trautmann Oct. 11, 2016

Oct. 11, 2016

Hi, After upgrading to scikit-learn 0.18 HashingVectorizer is about 10 times slower. Before: scikit-learn 0.17. Numpy 1.11.2. Python 3.5.2 AMD64 Vectorizing 20newsgroup 11314 documents Vectorization completed in 4.594092130661011 seconds, resulting shape (11314, 1048576) After upgrade: scikit-learn 0.18. Numpy 1.11.2. Python 3.5.2 AMD64 Vectorizing 20newsgroup 11314 documents Vectorization completed in 43.587692737579346 seconds, resulting shape (11314, 1048576) Code: import time, sklearn, platform, numpy from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import HashingVectorizer data_train = fetch_20newsgroups(subset='train', shuffle=True, random_state=42) print('scikit-learn {}. Numpy {}. Python {} {}'.format(sklearn.__version__, numpy.version.full_version, platform.python_version(), platform.machine())) vectorizer = HashingVectorizer() print("Vectorizing 20newsgroup {} documents".format(len(data_train.data))) start = time.time() data = vectorizer.fit_transform(data_train.data) print("Vectorization completed in ", time.time() - start, ' seconds, resulting shape ', data.shape) Should I submit a bug report? Thank you, Gabriel Trautmann

6 7

ANN Scikit-learn 0.18 released
by Andreas Mueller Oct. 11, 2016

Oct. 11, 2016

Hi everybody. I'm happy to announce scikit-learn 0.18 has been released today. You can install from pipy or anaconda.org: pip install --upgrade scikit-learn --no-deps or if you prefer conda: conda update scikit-learn A big thank you to everybody who contributed. This one took us a while, but I think it's worth the wait. Highlights include: - A new GaussianProcessClassifier and GaussianProcessRegressor to learn complex kernels! - A much improved GaussianMixture and BayesianGaussianMixture mixture models. - We moved the content of the grid_search, cross_validation and validation_curve modules to the new model_selection module. - A Multi-layer perceptron. and soo much more that it's impossible to summarize. Check out the full changelog here: http://scikit-learn.org/stable/whats_new.html#version-0-18 Please don't update a (ana)conda installation using pip, as that might lead to problems. Let us know any issues you have on the issue tracker: https://github.com/scikit-learn/scikit-learn/issues Enjoy! Andy

10 13

Welcome Raghav to the core-dev team
by Gael Varoquaux Oct. 11, 2016

Oct. 11, 2016

Hi, We have the pleasure to welcome Raghav RV to the core-dev team. Raghav (@raghavrv) has been working on scikit-learn for more than a year. In particular, he implemented the rewrite of the cross-validation utilities, which is quite dear to my heart. Welcome Raghav! Gaël

19 18