[scikit-learn] urgent help in scikit-learn

Wed Apr 5 17:48:17 EDT 2017

Also, in general it's not appropriate to repeatedly ping someone on this
mailing list for 'urgent help.'

On Wed, Apr 5, 2017 at 8:30 AM, Shane Grigsby <shane.grigsby at colorado.edu>
wrote:

> Hi Shuchi,
> You probably want to query the Statsmodels community for this; they have a
> google groups board here:
>
> https://groups.google.com/forum/#!forum/pystatsmodels
>
> Cheers,
> Shane
>
>
> On 04/05, Shuchi Mala wrote:
>
>> Hi Raschka,
>>
>> I need an urgent help. how I can use   Statsmodels Poisson function
>> function (statsmodels.genmod.families.Poisson) with Sci-Kit Learn's cross
>> validation metrics (cross_val_score, ShuffleSplit, cross_val_predict)?
>>
>> With Best Regards,
>> Shuchi  Mala
>> Research Scholar
>> Department of Civil Engineering
>> MNIT Jaipur
>>
>>
>> On Tue, Apr 4, 2017 at 2:05 PM, Shuchi Mala <shuchi.23 at gmail.com> wrote:
>>
>> Hi Raschka,
>>>
>>> I need an urgent help. how I can use   Statsmodels Poisson function
>>> function (statsmodels.genmod.families.Poisson) with Sci-Kit Learn's
>>> cross
>>> validation metrics (cross_val_score, ShuffleSplit, cross_val_predict)?
>>>
>>> With Best Regards,
>>> Shuchi  Mala
>>> Research Scholar
>>> Department of Civil Engineering
>>> MNIT Jaipur
>>>
>>>
>>> On Tue, Apr 4, 2017 at 9:15 AM, Shuchi Mala <shuchi.23 at gmail.com> wrote:
>>>
>>> Hi Raschka,
>>>>
>>>> I want to know how to use cross validation when other regression model
>>>> such as poisson is used in place of linear?
>>>>
>>>> Kindly help.
>>>>
>>>> With Best Regards,
>>>> Shuchi  Mala
>>>> Research Scholar
>>>> Department of Civil Engineering
>>>> MNIT Jaipur
>>>>
>>>>
>>>> On Mon, Apr 3, 2017 at 8:05 PM, Sebastian Raschka <se.raschka at gmail.com
>>>> >
>>>> wrote:
>>>>
>>>> Don’t get me wrong, but you’d have to either manually label them
>>>>> yourself, asking domain experts, or use platforms like Amazon Turk (or
>>>>> collect them in some other way).
>>>>>
>>>>> > On Apr 3, 2017, at 7:38 AM, Shuchi Mala <shuchi.23 at gmail.com> wrote:
>>>>> >
>>>>> > How can I get  ground truth labels of the training examples in my
>>>>> dataset?
>>>>> >
>>>>> > With Best Regards,
>>>>> > Shuchi  Mala
>>>>> > Research Scholar
>>>>> > Department of Civil Engineering
>>>>> > MNIT Jaipur
>>>>> >
>>>>> >
>>>>> > On Fri, Mar 31, 2017 at 8:17 PM, Sebastian Raschka <
>>>>> se.raschka at gmail.com> wrote:
>>>>> > Hi, Shuchi,
>>>>> >
>>>>> > regarding labels_true: you’d only be able to compute the rand index
>>>>> adjusted for chance if you have the ground truth labels iof the
>>>>> training
>>>>> examples in your dataset.
>>>>> >
>>>>> > The second parameter, labels_pred, takes in the predicted cluster
>>>>> labels (indices) that you got from the clustering. E.g,
>>>>> >
>>>>> > dbscn = DBSCAN()
>>>>> > labels_pred = dbscn.fit(X).predict(X)
>>>>> >
>>>>> > Best,
>>>>> > Sebastian
>>>>> >
>>>>> >
>>>>> > > On Mar 31, 2017, at 12:02 AM, Shuchi Mala <shuchi.23 at gmail.com>
>>>>> wrote:
>>>>> > >
>>>>> > > Thank you so much for your quick reply. I have one more doubt. The
>>>>> below statement is used to calculate rand score.
>>>>> > >
>>>>> > > metrics.adjusted_rand_score(labels_true, labels_pred)
>>>>> > >  In my case what will be labels_true and labels_pred and how I will
>>>>> calculate labels_pred?
>>>>> > >
>>>>> > > With Best Regards,
>>>>> > > Shuchi  Mala
>>>>> > > Research Scholar
>>>>> > > Department of Civil Engineering
>>>>> > > MNIT Jaipur
>>>>> > >
>>>>> > >
>>>>> > > On Thu, Mar 30, 2017 at 8:38 PM, Shane Grigsby <
>>>>> shane.grigsby at colorado.edu> wrote:
>>>>> > > Since you're using lat / long coords, you'll also want to convert
>>>>> them to radians and specify 'haversine' as your distance metric; i.e. :
>>>>> > >
>>>>> > >    coords = np.vstack([lats.ravel(),longs.ravel()]).T
>>>>> > >    coords *= np.pi / 180. # to radians
>>>>> > >
>>>>> > > ...and:
>>>>> > >
>>>>> > >    db = DBSCAN(eps=0.3, min_samples=10, metric='haversine')
>>>>> > >    # replace eps and min_samples as appropriate
>>>>> > >    db.fit(coords)
>>>>> > >
>>>>> > > Cheers,
>>>>> > > Shane
>>>>> > >
>>>>> > >
>>>>> > > On 03/30, Sebastian Raschka wrote:
>>>>> > > Hi, Shuchi,
>>>>> > >
>>>>> > > 1. How can I add data to the data set of the package?
>>>>> > >
>>>>> > > You don’t need to add your dataset to the dataset module to run
>>>>> your
>>>>> analysis. A convenient way to load it into a numpy array would be via
>>>>> pandas. E.g.,
>>>>> > >
>>>>> > > import pandas as pd
>>>>> > > df = pd.read_csv(‘your_data.txt', delimiter=r"\s+”)
>>>>> > > X = df.values
>>>>> > >
>>>>> > > 2. How I can calculate Rand index for my data?
>>>>> > >
>>>>> > > After you ran the clustering, you can use the “adjusted_rand_score”
>>>>> function, e.g., see
>>>>> > > http://scikit-learn.org/stable/modules/clustering.html#adjus
>>>>> ted-rand-score
>>>>> > >
>>>>> > > 3. How to use make_blobs command for my data?
>>>>> > >
>>>>> > > The make_blobs command is just a utility function to create
>>>>> toydatasets, you wouldn’t need it in your case since you already have
>>>>> “real” data.
>>>>> > >
>>>>> > > Best,
>>>>> > > Sebastian
>>>>> > >
>>>>> > >
>>>>> > > On Mar 30, 2017, at 4:51 AM, Shuchi Mala <shuchi.23 at gmail.com>
>>>>> wrote:
>>>>> > >
>>>>> > > Hi everyone,
>>>>> > >
>>>>> > > I have the data with following attributes: (Latitude, Longitude).
>>>>> Now I am performing clustering using DBSCAN for my data. I have
>>>>> following
>>>>> doubts:
>>>>> > >
>>>>> > > 1. How can I add data to the data set of the package?
>>>>> > > 2. How I can calculate Rand index for my data?
>>>>> > > 3. How to use make_blobs command for my data?
>>>>> > >
>>>>> > > Sample of my data is :
>>>>> > > Latitude        Longitude
>>>>> > > 37.76901        -122.429299
>>>>> > > 37.76904        -122.42913
>>>>> > > 37.76878        -122.429092
>>>>> > > 37.7763 -122.424249
>>>>> > > 37.77627        -122.424657
>>>>> > >
>>>>> > >
>>>>> > > With Best Regards,
>>>>> > > Shuchi  Mala
>>>>> > > Research Scholar
>>>>> > > Department of Civil Engineering
>>>>> > > MNIT Jaipur
>>>>> > >
>>>>> > > _______________________________________________
>>>>> > > 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
>>>>> > >
>>>>> > > --
>>>>> > > *PhD candidate & Research Assistant*
>>>>> > > *Cooperative Institute for Research in Environmental Sciences
>>>>> (CIRES)*
>>>>> > > *University of Colorado at Boulder*
>>>>> > >
>>>>> > > _______________________________________________
>>>>> > > 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
>>
>
>
> --
> *PhD candidate & Research Assistant*
> *Cooperative Institute for Research in Environmental Sciences (CIRES)*
> *University of Colorado at Boulder*
> _______________________________________________
> 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/20170405/23e998c1/attachment.html>