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 >>> >> >> >
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