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.grig...@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...@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...@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.rasc...@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...@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.rasc...@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...@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.grig...@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...@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@python.org >>>>> > > https://mail.python.org/mailman/listinfo/scikit-learn >>>>> > > >>>>> > > _______________________________________________ >>>>> > > scikit-learn mailing list >>>>> > > scikit-learn@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@python.org >>>>> > > https://mail.python.org/mailman/listinfo/scikit-learn >>>>> > > >>>>> > > _______________________________________________ >>>>> > > scikit-learn mailing list >>>>> > > scikit-learn@python.org >>>>> > > https://mail.python.org/mailman/listinfo/scikit-learn >>>>> > >>>>> > _______________________________________________ >>>>> > scikit-learn mailing list >>>>> > scikit-learn@python.org >>>>> > https://mail.python.org/mailman/listinfo/scikit-learn >>>>> > >>>>> > _______________________________________________ >>>>> > scikit-learn mailing list >>>>> > scikit-learn@python.org >>>>> > https://mail.python.org/mailman/listinfo/scikit-learn >>>>> >>>>> _______________________________________________ >>>>> scikit-learn mailing list >>>>> scikit-learn@python.org >>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>> >>>>> >>>> >>>> >>> > _______________________________________________ >> scikit-learn mailing list >> scikit-learn@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@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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