[scikit-learn] scikit-learn Digest, Vol 43, Issue 11
Mike Smith
javaeurusd at gmail.com
Sun Oct 6 04:55:28 EDT 2019
Can I call an MSExcel cell range in a function such as model.predict(),
instead of typing the data in for each element?
On Sat, Oct 5, 2019 at 11:58 AM <scikit-learn-request at python.org> wrote:
> Send scikit-learn mailing list submissions to
> scikit-learn at 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 at python.org
>
> You can reach the person managing the list at
> scikit-learn-owner at 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. Re: scikit-learn Digest, Vol 43, Issue 10 (Mike Smith)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sat, 5 Oct 2019 11:55:33 -0700
> From: Mike Smith <javaeurusd at gmail.com>
> To: scikit-learn at python.org
> Subject: Re: [scikit-learn] scikit-learn Digest, Vol 43, Issue 10
> Message-ID:
> <CAEWZffDWv8mOUVaKSSBzpiEebjcVrRD-t8zxuBSFCKxqTGi3=
> A at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> 1. Re: Can Scikit-learn decision tree (CART) have both
> continuous and categorical features? (C W)
>
> What I'd ask in reply to this is if regression and classification module
> results can be entered into an input for one resultant output.
>
>
>
> On Sat, Oct 5, 2019, 11:50 AM , <scikit-learn-request at python.org> wrote:
>
> > Send scikit-learn mailing list submissions to
> > scikit-learn at 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 at python.org
> >
> > You can reach the person managing the list at
> > scikit-learn-owner at 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. Re: Can Scikit-learn decision tree (CART) have both
> > continuous and categorical features? (C W)
> >
> >
> > ----------------------------------------------------------------------
> >
> > Message: 1
> > Date: Sat, 5 Oct 2019 14:50:09 -0400
> > From: C W <tmrsg11 at gmail.com>
> > To: Scikit-learn mailing list <scikit-learn at python.org>
> > Subject: Re: [scikit-learn] Can Scikit-learn decision tree (CART) have
> > both continuous and categorical features?
> > Message-ID:
> > <
> > CAE2FW2nHDJGNky2VWk-U8fU3gqwBqWEgidzTAWnUq+NzAK68VA at mail.gmail.com>
> > Content-Type: text/plain; charset="utf-8"
> >
> > Thanks, great material! I got pydotplus with graphviz to work.
> >
> > Using the code on sklean website [1], tree.plot_tree(clf.fit(iris.data,
> > iris.target)) gives an error:
> > AttributeError: module 'sklearn.tree' has no attribute 'plot_tree'
> >
> > Both my colleague and I got the same error message. Per this post
> > https://github.com/Microsoft/LightGBM/issues/1844, a PyPI update is
> > needed.
> >
> > [1] sklearn link:
> > https://scikit-learn.org/stable/modules/tree.html#classification
> >
> >
> > On Fri, Oct 4, 2019 at 11:52 PM Sebastian Raschka <
> > mail at sebastianraschka.com>
> > wrote:
> >
> > > The docs show a way such that you don't need to write it as png file
> > using
> > > tree.plot_tree:
> > > https://scikit-learn.org/stable/modules/tree.html#classification
> > >
> > > I don't remember why, but I think I had problems with that in the past
> (I
> > > think it didn't look so nice visually, but don't remember), which is
> why
> > I
> > > still stick to graphviz. For my use cases, it's not much hassle -- it
> > used
> > > to be a bit of a hassle to get GraphViz working, but now you can do
> > >
> > > conda install pydotplus
> > > conda install graphviz
> > >
> > > Coincidentally, I just made an example for a lecture I was teaching on
> > > Tue:
> > >
> >
> https://github.com/rasbt/stat479-machine-learning-fs19/blob/master/06_trees/code/06-trees_demo.ipynb
> > >
> > > Best,
> > > Sebastian
> > >
> > >
> > > > On Oct 4, 2019, at 10:09 PM, C W <tmrsg11 at gmail.com> wrote:
> > > >
> > > > On a separate note, what do you use for plotting?
> > > >
> > > > I found graphviz, but you have to first save it as a png on your
> > > computer. That's a lot work for just one plot. Is there something like
> a
> > > matplotlib?
> > > >
> > > > Thanks!
> > > >
> > > > On Fri, Oct 4, 2019 at 9:42 PM Sebastian Raschka <
> > > mail at sebastianraschka.com> wrote:
> > > > Yeah, think of it more as a computational workaround for achieving
> the
> > > same thing more efficiently (although it looks inelegant/weird)--
> > something
> > > like that wouldn't be mentioned in textbooks.
> > > >
> > > > Best,
> > > > Sebastian
> > > >
> > > > > On Oct 4, 2019, at 6:33 PM, C W <tmrsg11 at gmail.com> wrote:
> > > > >
> > > > > Thanks Sebastian, I think I get it.
> > > > >
> > > > > It's just have never seen it this way. Quite different from what
> I'm
> > > used in Elements of Statistical Learning.
> > > > >
> > > > > On Fri, Oct 4, 2019 at 7:13 PM Sebastian Raschka <
> > > mail at sebastianraschka.com> wrote:
> > > > > Not sure if there's a website for that. In any case, to explain
> this
> > > differently, as discussed earlier sklearn assumes continuous features
> for
> > > decision trees. So, it will use a binary threshold for splitting along
> a
> > > feature attribute. In other words, it cannot do sth like
> > > > >
> > > > > if x == 1 then right child node
> > > > > else left child node
> > > > >
> > > > > Instead, what it does is
> > > > >
> > > > > if x >= 0.5 then right child node
> > > > > else left child node
> > > > >
> > > > > These are basically equivalent as you can see when you just plug in
> > > values 0 and 1 for x.
> > > > >
> > > > > Best,
> > > > > Sebastian
> > > > >
> > > > > > On Oct 4, 2019, at 5:34 PM, C W <tmrsg11 at gmail.com> wrote:
> > > > > >
> > > > > > I don't understand your answer.
> > > > > >
> > > > > > Why after one-hot-encoding it still outputs greater than 0.5 or
> > less
> > > than? Does sklearn website have a working example on categorical input?
> > > > > >
> > > > > > Thanks!
> > > > > >
> > > > > > On Fri, Oct 4, 2019 at 3:48 PM Sebastian Raschka <
> > > mail at sebastianraschka.com> wrote:
> > > > > > Like Nicolas said, the 0.5 is just a workaround but will do the
> > > right thing on the one-hot encoded variables, here. You will find that
> > the
> > > threshold is always at 0.5 for these variables. I.e., what it will do
> is
> > to
> > > use the following conversion:
> > > > > >
> > > > > > treat as car_Audi=1 if car_Audi >= 0.5
> > > > > > treat as car_Audi=0 if car_Audi < 0.5
> > > > > >
> > > > > > or, it may be
> > > > > >
> > > > > > treat as car_Audi=1 if car_Audi > 0.5
> > > > > > treat as car_Audi=0 if car_Audi <= 0.5
> > > > > >
> > > > > > (Forgot which one sklearn is using, but either way. it will be
> > fine.)
> > > > > >
> > > > > > Best,
> > > > > > Sebastian
> > > > > >
> > > > > >
> > > > > >> On Oct 4, 2019, at 1:44 PM, Nicolas Hug <niourf at gmail.com>
> wrote:
> > > > > >>
> > > > > >>
> > > > > >>> But, decision tree is still mistaking one-hot-encoding as
> > > numerical input and split at 0.5. This is not right. Perhaps, I'm doing
> > > something wrong?
> > > > > >>
> > > > > >> You're not doing anything wrong, and neither is the tree. Trees
> > > don't support categorical variables in sklearn, so everything is
> treated
> > as
> > > numerical.
> > > > > >>
> > > > > >> This is why we do one-hot-encoding: so that a set of numerical
> > (one
> > > hot encoded) features can be treated as if they were just one
> categorical
> > > feature.
> > > > > >>
> > > > > >>
> > > > > >>
> > > > > >> Nicolas
> > > > > >>
> > > > > >> On 10/4/19 2:01 PM, C W wrote:
> > > > > >>> Yes, you are right. it was 0.5 and 0.5 for split, not 1.5. So,
> > > typo on my part.
> > > > > >>>
> > > > > >>> Looks like I did one-hot-encoding correctly. My new variable
> > names
> > > are: car_Audi, car_BMW, etc.
> > > > > >>>
> > > > > >>> But, decision tree is still mistaking one-hot-encoding as
> > > numerical input and split at 0.5. This is not right. Perhaps, I'm doing
> > > something wrong?
> > > > > >>>
> > > > > >>> Is there a good toy example on the sklearn website? I am only
> see
> > > this:
> > >
> >
> https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html
> > > .
> > > > > >>>
> > > > > >>> Thanks!
> > > > > >>>
> > > > > >>>
> > > > > >>>
> > > > > >>> On Fri, Oct 4, 2019 at 1:28 PM Sebastian Raschka <
> > > mail at sebastianraschka.com> wrote:
> > > > > >>> Hi,
> > > > > >>>
> > > > > >>>> The funny part is: the tree is taking one-hot-encoding (BMW=0,
> > > Toyota=1, Audi=2) as numerical values, not category.The tree splits at
> > 0.5
> > > and 1.5
> > > > > >>>
> > > > > >>> that's not a onehot encoding then.
> > > > > >>>
> > > > > >>> For an Audi datapoint, it should be
> > > > > >>>
> > > > > >>> BMW=0
> > > > > >>> Toyota=0
> > > > > >>> Audi=1
> > > > > >>>
> > > > > >>> for BMW
> > > > > >>>
> > > > > >>> BMW=1
> > > > > >>> Toyota=0
> > > > > >>> Audi=0
> > > > > >>>
> > > > > >>> and for Toyota
> > > > > >>>
> > > > > >>> BMW=0
> > > > > >>> Toyota=1
> > > > > >>> Audi=0
> > > > > >>>
> > > > > >>> The split threshold should then be at 0.5 for any of these
> > > features.
> > > > > >>>
> > > > > >>> Based on your email, I think you were assuming that the DT does
> > > the one-hot encoding internally, which it doesn't. In practice, it is
> > hard
> > > to guess what is a nominal and what is a ordinal variable, so you have
> to
> > > do the onehot encoding before you give the data to the decision tree.
> > > > > >>>
> > > > > >>> Best,
> > > > > >>> Sebastian
> > > > > >>>
> > > > > >>>> On Oct 4, 2019, at 11:48 AM, C W <tmrsg11 at gmail.com> wrote:
> > > > > >>>>
> > > > > >>>> I'm getting some funny results. I am doing a regression
> decision
> > > tree, the response variables are assigned to levels.
> > > > > >>>>
> > > > > >>>> The funny part is: the tree is taking one-hot-encoding (BMW=0,
> > > Toyota=1, Audi=2) as numerical values, not category.
> > > > > >>>>
> > > > > >>>> The tree splits at 0.5 and 1.5. Am I doing one-hot-encoding
> > > wrong? How does the sklearn know internally 0 vs. 1 is categorical, not
> > > numerical?
> > > > > >>>>
> > > > > >>>> In R for instance, you do as.factor(), which explicitly states
> > > the data type.
> > > > > >>>>
> > > > > >>>> Thank you!
> > > > > >>>>
> > > > > >>>>
> > > > > >>>> On Wed, Sep 18, 2019 at 11:13 AM Andreas Mueller <
> > > t3kcit at gmail.com> wrote:
> > > > > >>>>
> > > > > >>>>
> > > > > >>>> On 9/15/19 8:16 AM, Guillaume Lema?tre wrote:
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>> On Sat, 14 Sep 2019 at 20:59, C W <tmrsg11 at gmail.com> wrote:
> > > > > >>>>> Thanks, Guillaume.
> > > > > >>>>> Column transformer looks pretty neat. I've also heard though,
> > > this pipeline can be tedious to set up? Specifying what you want for
> > every
> > > feature is a pain.
> > > > > >>>>>
> > > > > >>>>> It would be interesting for us which part of the pipeline is
> > > tedious to set up to know if we can improve something there.
> > > > > >>>>> Do you mean, that you would like to automatically detect of
> > > which type of feature (categorical/numerical) and apply a
> > > > > >>>>> default encoder/scaling such as discuss there:
> > >
> >
> https://github.com/scikit-learn/scikit-learn/issues/10603#issuecomment-401155127
> > > > > >>>>>
> > > > > >>>>> IMO, one a user perspective, it would be cleaner in some
> cases
> > > at the cost of applying blindly a black box
> > > > > >>>>> which might be dangerous.
> > > > > >>>> Also see
> > >
> >
> https://amueller.github.io/dabl/dev/generated/dabl.EasyPreprocessor.html#dabl.EasyPreprocessor
> > > > > >>>> Which basically does that.
> > > > > >>>>
> > > > > >>>>
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>> Jaiver,
> > > > > >>>>> Actually, you guessed right. My real data has only one
> > numerical
> > > variable, looks more like this:
> > > > > >>>>>
> > > > > >>>>> Gender Date Income Car Attendance
> > > > > >>>>> Male 2019/3/01 10000 BMW Yes
> > > > > >>>>> Female 2019/5/02 9000 Toyota No
> > > > > >>>>> Male 2019/7/15 12000 Audi Yes
> > > > > >>>>>
> > > > > >>>>> I am predicting income using all other categorical variables.
> > > Maybe it is catboost!
> > > > > >>>>>
> > > > > >>>>> Thanks,
> > > > > >>>>>
> > > > > >>>>> M
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>> On Sat, Sep 14, 2019 at 9:25 AM Javier L?pez <jlopez at ende.cc
> >
> > > wrote:
> > > > > >>>>> If you have datasets with many categorical features, and
> > perhaps
> > > many categories, the tools in sklearn are quite limited,
> > > > > >>>>> but there are alternative implementations of boosted trees
> that
> > > are designed with categorical features in mind. Take a look
> > > > > >>>>> at catboost [1], which has an sklearn-compatible API.
> > > > > >>>>>
> > > > > >>>>> J
> > > > > >>>>>
> > > > > >>>>> [1] https://catboost.ai/
> > > > > >>>>>
> > > > > >>>>> On Sat, Sep 14, 2019 at 3:40 AM C W <tmrsg11 at gmail.com>
> wrote:
> > > > > >>>>> Hello all,
> > > > > >>>>> I'm very confused. Can the decision tree module handle both
> > > continuous and categorical features in the dataset? In this case, it's
> > just
> > > CART (Classification and Regression Trees).
> > > > > >>>>>
> > > > > >>>>> For example,
> > > > > >>>>> Gender Age Income Car Attendance
> > > > > >>>>> Male 30 10000 BMW Yes
> > > > > >>>>> Female 35 9000 Toyota No
> > > > > >>>>> Male 50 12000 Audi Yes
> > > > > >>>>>
> > > > > >>>>> According to the documentation
> > >
> >
> https://scikit-learn.org/stable/modules/tree.html#tree-algorithms-id3-c4-5-c5-0-and-cart
> > ,
> > > it can not!
> > > > > >>>>>
> > > > > >>>>> It says: "scikit-learn implementation does not support
> > > categorical variables for now".
> > > > > >>>>>
> > > > > >>>>> Is this true? If not, can someone point me to an example? If
> > > yes, what do people do?
> > > > > >>>>>
> > > > > >>>>> Thank you very much!
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>> _______________________________________________
> > > > > >>>>> scikit-learn mailing list
> > > > > >>>>> scikit-learn at python.org
> > > > > >>>>> https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > >>>>> _______________________________________________
> > > > > >>>>> scikit-learn mailing list
> > > > > >>>>> scikit-learn at python.org
> > > > > >>>>> https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > >>>>> _______________________________________________
> > > > > >>>>> scikit-learn mailing list
> > > > > >>>>> scikit-learn at python.org
> > > > > >>>>> https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>> --
> > > > > >>>>> Guillaume Lemaitre
> > > > > >>>>> INRIA Saclay - Parietal team
> > > > > >>>>> Center for Data Science Paris-Saclay
> > > > > >>>>> https://glemaitre.github.io/
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>> _______________________________________________
> > > > > >>>>> scikit-learn mailing list
> > > > > >>>>>
> > > > > >>>>> scikit-learn at python.org
> > > > > >>>>> https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > >>>>
> > > > > >>>> _______________________________________________
> > > > > >>>> scikit-learn mailing list
> > > > > >>>> scikit-learn at python.org
> > > > > >>>> https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > >>>> _______________________________________________
> > > > > >>>> scikit-learn mailing list
> > > > > >>>> scikit-learn at python.org
> > > > > >>>> https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > >>>
> > > > > >>> _______________________________________________
> > > > > >>> scikit-learn mailing list
> > > > > >>> scikit-learn at python.org
> > > > > >>> https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > >>>
> > > > > >>>
> > > > > >>> _______________________________________________
> > > > > >>> scikit-learn mailing list
> > > > > >>>
> > > > > >>> scikit-learn at python.org
> > > > > >>> https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > >> _______________________________________________
> > > > > >> scikit-learn mailing list
> > > > > >> scikit-learn at python.org
> > > > > >> https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > >
> > > > > > _______________________________________________
> > > > > > scikit-learn mailing list
> > > > > > scikit-learn at python.org
> > > > > > https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > > _______________________________________________
> > > > > > scikit-learn mailing list
> > > > > > scikit-learn at python.org
> > > > > > https://mail.python.org/mailman/listinfo/scikit-learn
> > > > >
> > > > > _______________________________________________
> > > > > scikit-learn mailing list
> > > > > scikit-learn at python.org
> > > > > https://mail.python.org/mailman/listinfo/scikit-learn
> > > > > _______________________________________________
> > > > > scikit-learn mailing list
> > > > > scikit-learn at python.org
> > > > > https://mail.python.org/mailman/listinfo/scikit-learn
> > > >
> > > > _______________________________________________
> > > > scikit-learn mailing list
> > > > scikit-learn at python.org
> > > > https://mail.python.org/mailman/listinfo/scikit-learn
> > > > _______________________________________________
> > > > scikit-learn mailing list
> > > > scikit-learn at python.org
> > > > https://mail.python.org/mailman/listinfo/scikit-learn
> > >
> > > _______________________________________________
> > > scikit-learn mailing list
> > > scikit-learn at python.org
> > > https://mail.python.org/mailman/listinfo/scikit-learn
> > >
> > -------------- next part --------------
> > An HTML attachment was scrubbed...
> > URL: <
> >
> http://mail.python.org/pipermail/scikit-learn/attachments/20191005/7234be32/attachment.html
> > >
> >
> > ------------------------------
> >
> > Subject: Digest Footer
> >
> > _______________________________________________
> > scikit-learn mailing list
> > scikit-learn at python.org
> > https://mail.python.org/mailman/listinfo/scikit-learn
> >
> >
> > ------------------------------
> >
> > End of scikit-learn Digest, Vol 43, Issue 10
> > ********************************************
> >
> -------------- next part --------------
> An HTML attachment was scrubbed...
> URL: <
> http://mail.python.org/pipermail/scikit-learn/attachments/20191005/14272924/attachment.html
> >
>
> ------------------------------
>
> Subject: Digest Footer
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
>
> ------------------------------
>
> End of scikit-learn Digest, Vol 43, Issue 11
> ********************************************
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20191006/ab202d09/attachment-0001.html>
More information about the scikit-learn
mailing list