🌐 Mailman 3 September 2017 - scikit-learn - python.org

New commits mailing list
by Andreas Mueller Sept. 14, 2017

Sept. 14, 2017

Hey all. I set up scikit-learn-commits(a)python.org to track commits to the scikit-learn repo. You can subscribe here: https://mail.python.org/mm3/mailman3/lists/scikit-learn-commits.python.org/ Cheers, Andy

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Accessing Clustering Feature Tree in Birch
by Sema Atasever Sept. 13, 2017

Sept. 13, 2017

Dear scikit-learn members, Considering the "CF-tree" data structure : - How can i *access Clustering Feature Tree* in Birch? - For example, how many clusters are there in the hierarchy under the *root node* and what are the data samples in this cluster? - Can I get them separately for 3 trees? Best.

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how to make result less number of group with NearestNeighbors?
by Martin Lee Sept. 11, 2017

Sept. 11, 2017

nbrs = NearestNeighbors(n_neighbors=10,radius=100.0,metric='euclidean',algorithm='ball_tree').fit(testing1) distances, indices = nbrs.kneighbors(testing1) just expect when each point distance less than 100 then group into one group Martin

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control value range of MLPRegressor predictions
by Thomas Evangelidis Sept. 10, 2017

Sept. 10, 2017

Greetings, Is there any way to force the MLPRegressor to make predictions in the same value range as the training data? For example, if the training data range between -5 and -9, I don't want the predictions to range between -820 and -800. In fact, some times I get anti-correlated predictions, for example between 800 and 820 and I have to change the sign in order to calculate correlations with experimental values. Is there a way to control the value range explicitly or implicitly (by post-processing the predictions)? thanks Thomas -- ====================================================================== Dr Thomas Evangelidis Post-doctoral Researcher CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/2S049, 62500 Brno, Czech Republic email: tevang(a)pharm.uoa.gr tevang3(a)gmail.com website: https://sites.google.com/site/thomasevangelidishomepage/

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combining datasets from different sources
by Thomas Evangelidis Sept. 7, 2017

Sept. 7, 2017

Greetings, I am working on a problem that involves predicting the binding affinity of small molecules on a receptor structure (is regression problem, not classification). I have multiple small datasets of molecules with measured binding affinities on a receptor, but each dataset was measured in different experimental conditions and therefore I cannot use them all together as trainning set. So, instead of using them individually, I was wondering whether there is a method to combine them all into a super training set. The first way I could think of is to convert the binding affinities to Z-scores and then combine all the small datasets of molecules. But this is would be inaccurate because, firstly the datasets are very small (10-50 molecules each), and secondly, the range of binding affinities differs in each experiment (some datasets contain really strong binders, while others do not, etc.). Is there any other approach to combine datasets with values coming from different sources? Maybe if someone points me to the right reference I could read and understand if it is applicable to my case. Thanks, Thomas -- ====================================================================== Dr Thomas Evangelidis Post-doctoral Researcher CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/2S049, 62500 Brno, Czech Republic email: tevang(a)pharm.uoa.gr tevang3(a)gmail.com website: https://sites.google.com/site/thomasevangelidishomepage/

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using a predictor as transformer
by Lefevre, Augustin Sept. 6, 2017

Sept. 6, 2017

Hi all, I am playing with the pipeline features of sklearn and it seems that I can't use a prediction algorithm as intermediate step. For instance, in the example below I use the output of a lasso as an additional feature to feed a random forest, in such a way that feature selection the Lasso does some preliminary feature selection. But I get a : "TypeError: All estimators should implement fit and transform." So I would like to add a transform method to the Lasso estimator so that it can be used in a FeatureUnion. Is that possible ? Best regards X=np.hstack((np.random.randn(500,10),np.random.randint(0,10,(500,10)))) # regressor variables y=np.random.randn(500) # target variable ct_get = FunctionTransformer(lambda d:d[0:10]) # transformer to extract continuous variables dt_get = FunctionTransformer(lambda d:d[11:20]) # transformer to extract discrete variables # first step is a regression pipeline reg = Pipeline([('ct_vars',ct_get),('scaler',StandardScaler()),('poly',PolynomialFeatures(degree=3)),('lasso',Lasso())]) # A random forest feeds on the discrete part of the data + one continuous variable estimator = Pipeline([('level1',FeatureUnion([('dt_vars',dt_get),('reg',reg)])),('rf',RandomForestRegressor())]) estimator.fit(X,y) print "R^2 score is :",estimator.score(X,y) Augustin LEFEVRE| Consultant Senior | Ykems | - M : +33 7 77 97 94 89 | alefevre(a)ykems.com<mailto:alefevre@ykems.com> | www.ykems.com<http://www.ykems.com/> [https://www.beijaflore.com/_mailing/signature/image001.png]<http://www.linkedin.com/company/beijaflore?trk=top_nav_home> [https://www.beijaflore.com/_mailing/signature/image002.png] <https://twitter.com/BeijafloreGroup> [https://www.beijaflore.com/_mailing/signature/image003.png] <https://www.facebook.com/BeijafloreGroup> [https://www.beijaflore.com/_mailing/signature/image004.png] <https://www.youtube.com/user/ComBeijaflore> P Save a tree ! Think before you print SECURE BUSINESS This message and its attachment contain information that may be privileged or confidential and is the property of Beijaflore. It is intended only for the person to whom it is addressed. If you are not the intended recipient, you are not authorized to read, print, retain, copy, disseminate, distribute, use or rely on the information contained in this email. If you receive this message in error, please notify the sender immediately and delete all copies of this message.

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Need suggestions on the example about discretization
by qinhanmin2005＠sina.com Sept. 6, 2017

Sept. 6, 2017

Scikit-learn recently supported discretization(KBinsDiscretizer, see discrete branch) and we need an example to illustrate the usage of it. I have proposed a draft in https://github.com/scikit-learn/scikit-learn/issues/9339:(1)use the iris dataset (only use two features)(2)plot the data before and after discretization(3)train a classifier using the data before and after discretization and compare the result from cross validation Since I'm not an expert of machine learning, I'm wondering whether it is a good example. Could someone from the community provide some ideas or suggestions? Thanks a lot.

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Problem found when testing DecisionTreeClassifier within the source folder
by hanzi mao Sept. 5, 2017

Sept. 5, 2017

Hi, I am researching on the source code of DecisionTree recently. Here are the things I tried. 1. Downloaded source code from github. 2. run "python setup.py build_ext --inplace" to compile the sources in the unzipped source folder. 3. Try the following codes to see whether it works. Here I changed the name of the sklearn folder to sklearn1 to differentiate it from the one installed. >>> from sklearn1 import tree >>> from sklearn.datasets import load_iris >>> iris = load_iris() >>> clf = tree.DecisionTreeClassifier() >>> clf = clf.fit(iris.data, iris.target) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "sklearn1\tree\tree.py", line 790, in fit X_idx_sorted=X_idx_sorted) File "sklearn1\tree\tree.py", line 341, in fit self.presort) TypeError: Argument 'criterion' has incorrect type (expected sklearn.tree._criterion.Criterion, got sklearn.tree._criterion.Gini) Then a weird error happened. Actually I also tried the newest stable version of scikit-learn earlier today. It had the same error. So I was thinking maybe try the newest version in github might help. Unlikely, it didn't. I have limited knowledge about the source code of scikit-learn. I am wondering if anyone could help me with this. Thanks! Best, Hanna

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