Ack, should've mentioned you can do: from sklearn.externals import joblib
since it is a sklearn dependency. That way you won't need to install joblib separately On Aug 21, 2017 10:38, "David Nicholson" <nichol...@gmail.com> wrote: > Hi Sema, > > You can save using pickle from the Python standard library, or using the > joblib library which is a dependency of sklearn (so you have it already). > > The sklearn docs show examples of saving models but it will work for your > predict results too: > http://scikit-learn.org/stable/modules/model_persistence.html > > You'd just do something like: > import joblib > ... > # your code here > ... > birch_predict = brc.predict(X) > filename = 'predictions' > joblib.dump(birch_predict, filename) > > And you can get the values back into memory with joblib.load > > Hth > --David (list lurker) > > On Aug 21, 2017 10:13, "Sema Atasever" <s.atase...@gmail.com> wrote: > > Dear scikit-learn developers, > > I have a text file where the columns represent the 22 features and the > rows represent the amino asid . (you can see in the attachment) > > > I want to apply hierarchical clustering to this database usign > *sklearn.cluster.Birch > algorithm.* > > There are too many prediction results and it is not possible to see them > on the screen. > How can i write the birch prediction results to the file? > > I would appreciate if you could advise on some methods. > Thanks. > > *Birch Codes:* > from sklearn.cluster import Birch > import numpy as np > > X=np.loadtxt(open("C:\class1.txt", "rb"), delimiter=";") > > brc = Birch(branching_factor=50, n_clusters=None, > threshold=0.5,compute_labels=True,copy=True) > > brc.fit(X) > > centroids = brc.subcluster_centers_ > > labels = brc.subcluster_labels_ > n_clusters = np.unique(labels).size > brc.predict(X) > > print("\n brc.predict(X)") > print(brc.predict(X)) > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > > >
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