[scikit-learn] urgent help in scikit-learn
Shuchi Mala
shuchi.23 at gmail.com
Tue Apr 4 23:57:10 EDT 2017
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
>>> > >
>>> > > _______________________________________________
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>>> > >
>>> > > --
>>> > > *PhD candidate & Research Assistant*
>>> > > *Cooperative Institute for Research in Environmental Sciences
>>> (CIRES)*
>>> > > *University of Colorado at Boulder*
>>> > >
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