🌐 Mailman 3 October 2018 - scikit-learn - python.org

Pipegraph example: KMeans + LDA
by Manuel Castejón Limas Nov. 6, 2018

Nov. 6, 2018

Dear all, as a way of improving the documentation of PipeGraph we intend to provide more examples of its usage. It was a popular demand to show application cases to motivate its usage, so here it is a very simple case with two steps: a KMeans followed by a LDA. https://mcasl.github.io/PipeGraph/auto_examples/plot_Finding_Number_of_clus… This short example points out the following challenges: - KMeans is not a transformer but an estimator - LDA score function requires the y parameter, while its input does not come from a known set of labels, but from the previous KMeans - Moreover, the GridSearchCV.fit call would also require a 'y' parameter - It would be nice to have access to the output of the KMeans step as well. PipeGraph is capable of addressing these challenges. The rationale for this example lies in the identification-reconstruction realm. In a scenario where the class labels are unknown, we might want to associate the quality of the clustering structure to the capability of a later model to be able to reconstruct this structure. So the basic idea here is that if LDA is capable of getting good results it was because the information of the KMeans was good enough for that purpose, hinting the discovery of a good structure.

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Elbow method function for K-means procedure
by Maiia Bakhova Nov. 5, 2018

Nov. 5, 2018

Hello everybody! I would like to offer a new feature for consideration. Here is my presentation: https://github.com/Mathemilda/ElbowMethodForK-means/blob/master/Elbow_Metho… Thanks for your time! If the feature is to be accepted, can you please tell me what are conventions if any for such function and if there is a template or other helpful material. Best regards, Maiia Bakhova --------------------- Mathematician in Data Science <http://myabakhova.blogspot.com/> https://www.linkedin.com/in/myabakhova

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Can I use Sklearn Porter to Generate C++ version of Random Forest Predict function
by Chidhambaranathan R Nov. 1, 2018

Nov. 1, 2018

Hi, I'd like to know if I can use sklearn_porter to generate the C++ version of Random Forest Regression Predict function. If sklearn_porter doesn't work, is there any possible alternatives to generate c++ implementation of RF Regressor Predict function? Thanks. -- Regards, Chidhambaranathan R, PhD Student, Electrical and Computer Engineering, Utah State University

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Re: [scikit-learn] Fairness Metrics
by Feldman, Joshua Oct. 30, 2018

Oct. 30, 2018

Hi Andy, Yes, good point and thank you for your thoughts. The Aequitas project stood out to me more because of their flowchart than their auditing software because, as you mention, you always fail the report if you include all the measures! Just as with choosing a machine learning algorithm, there isn't a one size fits all solution to ML ethics, as evidenced by the contradicting metrics. A reason why I think implementing fairness metrics in sklearn might be a good idea is that it would empower people to choose the metric that's relevant to them and their users. If we were to implement these metrics, it would be very important to clarify this in the documentation. Tools that could change predictions to be fair according to one of these metrics would also be very cool. In the same vein as my thinking above, we would need to be careful about giving a false sense of security with the "fair" algorithms such a tool would produce. If you don't think now is the time to add these metrics, is there anything I could do to move this along? Best, Josh

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Fairness Metrics
by Feldman, Joshua Oct. 30, 2018

Oct. 30, 2018

Hi, I was wondering if there's any interest in adding fairness metrics to sklearn. Specifically, I was thinking of implementing the metrics described here: https://dsapp.uchicago.edu/projects/aequitas/ I recognize that these metrics are extremely simple to calculate, but given that sklearn is the standard machine learning package in python, I think it would be very powerful to explicitly include algorithmic fairness - it would make these methods more accessible and, as a matter of principle, demonstrate that ethics is part of ML and not an afterthought. I would love to hear the groups' thoughts and if there's interest in such a feature. Thanks! Josh

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Google Season of Docs
by Andreas Mueller Oct. 30, 2018

Oct. 30, 2018

Hey. Are we interested in the Google Season of Docs? https://docs.google.com/forms/d/e/1FAIpQLSf-njReSfmp5i2olgmsDzrFR0Ll0UB5LkC… https://docs.google.com/presentation/d/1ABqCc5uAoQv9aqGCxmNqOJ9S_Tst-adNV3f… It requires a mentor, which has been an issue in the past. But it looks like the idea is to have professionals partner up with projects, not students. The other problem would of course be formulating a clearly defined project. I think we could probably use some restructuring, or more focused tutorials. Wdyt? Andy

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Strange code but that works
by Louis Abraham Oct. 28, 2018

Oct. 28, 2018

Hi, This is a code from sklearn.pipeline.Pipeline: @property def transform(self): """Apply transforms, and transform with the final estimator This also works where final estimator is ``None``: all prior transformations are applied. Parameters ---------- X : iterable Data to transform. Must fulfill input requirements of first step of the pipeline. Returns ------- Xt : array-like, shape = [n_samples, n_transformed_features] """ # _final_estimator is None or has transform, otherwise attribute error # XXX: Handling the None case means we can't use if_delegate_has_method if self._final_estimator is not None: self._final_estimator.transform return self._transform I don't understand why `self._final_estimator.transform` can be returned, ignoring all the previous transformers. However, when testing it works: ``` >>> p = make_pipeline(FunctionTransformer(lambda x: 2*x), FunctionTransformer(lambda x: x-1)) >>> p.transform(np.array([[1,2]])) array([[1, 3]]) ``` Could somebody explain that to me? Best, Louis Abraham

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How does the random state influence the decision tree splits?
by Sebastian Raschka Oct. 28, 2018

Oct. 28, 2018

Hi all, when I was implementing a bagging classifier based on scikit-learn's DecisionTreeClassifier, I noticed that the results were not deterministic and found that this was due to the random_state in the DescisionTreeClassifier (which is set to None by default). I am wondering what exactly this random state is used for? I can imaging it being used for resolving ties if the information gain for multiple features is the same, or it could be that the feature splits of continuous features is different? (I thought the heuristic is to sort the features and to consider those feature values next to each associated with examples that have different class labels -- but is there maybe some random subselection involved?) If someone knows more about this, where the random_state is used, I'd be happy to hear it :) Also, we could then maybe add the info to the DecisionTreeClassifier's docstring, which is currently a bit too generic to be useful, I think: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/tree.… random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Best, Sebastian

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Question about get_params / set_params
by Louis Abraham Oct. 28, 2018

Oct. 28, 2018

Hi, According to http://scikit-learn.org/0.16/developers/index.html#get-params-and-set-params <http://scikit-learn.org/0.16/developers/index.html#get-params-and-set-params>, get_params and set_params are used to clone estimators. However, I don't understand how it is used in FeatureUnion: `return self._get_params('transformer_list', deep=deep)` Why doesn't it contain other arguments like n_jobs and transformer_weights? Best Louis

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Sparse predict_proba and Fenchel-Young losses
by Mathieu Blondel Oct. 26, 2018

Oct. 26, 2018

Hi, Most scikit-learn users who need predict_proba use the logistic regression class. We've released a new package implementing more loss functions useful for probabilistic classification. https://github.com/mblondel/fenchel-young-losses/ This is based on our recently proposed family of loss functions called "Fenchel-Young losses" [*]. Two distinguishing features that should be of interest: 1) You can call fit(X, Y) where Y is a n_samples array of label integers *or* Y is a n_samples x n_classes array containing *label proportions*. 2) predict_proba(X) is able to output *sparse* probabilities for some choices of loss functions (loss="sparsemax" or loss="tsallis"). This means that some classes may get *exactly* zero probability. Both features are especially useful in a multi-label setting. We've also released drop-in replacements for PyTorch and Tensorflow loss functions in the same package. Feedback welcome! Cheers, Mathieu [*] https://arxiv.org/abs/1805.09717

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