Mailman 3 January 2019 - scikit-learn

Using sklearn-crfsuite on Production Systems
by Astha Agarwal Aug. 5, 2019

Aug. 5, 2019

Hi, I'm wondering if anyone is using sklearn-crfsuite on production systems? Is this library suitable for usage in industry on production systems (and not academia) for non-big data problems? Thanks, Astha

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Does random forest work if there are very few features?
by lampahome Feb. 19, 2019

Feb. 19, 2019

I read doc and know tree-based model is determined by entropy or gini impurity. When model try to create leaf nodes, it will determine based on the feature, right? Ex: I have 2 features A,B, and I divide it with A. So I have left and right nodes based on A. It should have the best shape if I create nodes based on A, right? Now if I have 100 estimators but I only have two features, do I have different trees which are all based on feature A? or the shape of trees based on A are all the same cuz they were created by feature A? thx

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sklearn.cluster.OPTICS
by Mohit Srivastava Feb. 4, 2019

Feb. 4, 2019

Dear all, I want to use your clustering algorithm "sklearn.cluster.OPTICS". But it is not working and found that it's not available at the moment( found on the internet). Could you please help me with the issue? When would it be possible to use it? Please reply as soon as possible. thanks regards Mohit Srivastava

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Bounded logistical regression in Python
by Jaap van Kampen Jan. 31, 2019

Jan. 31, 2019

Hi there! The standard logistical regression solver <https://github.com/scikit-learn/scikit-learn/blob/7389dba/sklearn/linear_mo…> in scikit-learn assumes the regression equation:<p> P(X) = 1/ (1 + exp(b0 + b1*X1 + ... + bn*Xn))</p> .. and solves for the b's using various solver routines. For a specific project, I'd like to bound the regression equation between 0-a (instead of 0-1) and add a variable c to center an independent variable Xk, e.g.<p> P(X) = a / (1 + exp(b0 + b1*X1 + .. + bn*Xn) * (Xk - c))) </p> ... and solve for a, b's and c. Any thoughts/ideas on how to modify logistic.py to achieve this? I thought of modifying the expit <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.special.e…>function to reflect the changed equation. But how do a let the solvers know to also include the new variables a and c? Any scripts available that are able to handle my modified logistic regression equation?

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Can y of datasets be increasing/decreasing ratio when train regression model?
by lampahome Jan. 31, 2019

Jan. 31, 2019

I found many cases in kaggle to predict the quantity or trends. They all set the real quantity as y. But I have question is that does anyone set the changing ratio as y? Like: X y Day1 0.2 Day2 0.1 Day3 0.15 Day4 -0.1 y is the changing ratio compared with previous day. Why anybody set the real quantity(ex: sales, car numbers...etc) as y rather than changing ratio as y? I want to know it's based on experience or other reasons thx

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Is there rule to determine X and y when train regression?
by lampahome Jan. 29, 2019

Jan. 29, 2019

I found many example to predict stock, house prices, taxi fare...etc. The field of y almost like below: y : the price of the day And X maybe the day, param which can affect price...etc. Now I want to predict sales of multiple items of multiple stores. Is suitable to let decrease/increase ratio of sales be y? The reason I'm interesting is I don't know how to explain to other people that price as y is normal. So other people may have a question that can we let y be increase/decrease ratio? thx

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Google Cloud ML Engine Error with Sklearn
by Liam Geron Jan. 28, 2019

Jan. 28, 2019

Hi scikit learn contributors, I am currently attempting to transfer our preexisting models into cloud ML for scalability, however I am encountering bugs while running through some tutorial code found here ( https://github.com/GoogleCloudPlatform/cloudml-samples/blob/master/sklearn/… ). On both my local machine in a virtual environment and on the cloud shell I'm encountering errors when it comes to version creation and online prediction. For version creation on my local machine and on the cloud shell I'm encountering this error: *"ERROR: (gcloud.ml-engine.versions.create) Bad model detected with error: "Failed to load model: Could not load the model: /tmp/model/0001/model.joblib. 32. (Error code: 0)""* with Python 3.6.4 (local) and Python 3.5.6 (cloud shell) when running the command: *"gcloud ml-engine versions create $VERSION_NAME \* * --model $MODEL_NAME \* * --config config.yaml"* This is running with joblib version "0.13.1" and sklearn version "0.19.1". Any help would be greatly appreciated. Thank you, Liam Geron

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Regarding GSOC and open source contributions
by Suryoday Basak Jan. 27, 2019

Jan. 27, 2019

Dear Team, Could you let me know if scikit-learn might be a GSOC organization this year? I have a few proposal ideas in mind and have been working to implement certain methods over the existing project, and was wondering if I could talk to someone about how to go about things. Thank you. Regards, *Suryoday Basak* *Graduate Student*, Department of Computer Science and Engineering, *The University of Texas at Arlington* Website: *suryodaybasak.info <http://suryodaybasak.info>* Follow me on Medium: *https://medium.com/@suryodaybasak <https://medium.com/@suryodaybasak>*Astroinformatics Research Group: *http://astrirg.org <http://astrirg.org>* * <http://ascl.net/code/v/1475>*

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Imblearn: SMOTENC
by S Hamidizade Jan. 26, 2019

Jan. 26, 2019

Dear Scikit-learners Hi. I would greatly appreciate if you could let me know how to use SMOTENC. I wrote: num_indices1 = list(X.iloc[:,np.r_[0:94,95,97,100:123]].columns.values) cat_indices1 = list(X.iloc[:,np.r_[94,96,98,99,123:160]].columns.values) print(len(num_indices1)) print(len(cat_indices1)) pipeline=Pipeline(steps= [ # Categorical features ('feature_processing', FeatureUnion(transformer_list = [ ('categorical', MultiColumn(cat_indices1)), #numeric ('numeric', Pipeline(steps = [ ('select', MultiColumn(num_indices1)), ('scale', StandardScaler()) ])) ])), ('clf', rg) ] ) Therefore, as it is indicated I have 5 categorical features. Really, indices 123 to 160 are related to one categorical feature with 37 possible values which is converted into 37 columns using get_dummies. Sorry, I think SMOTENC should be inserted before the classifier ('clf', reg) but I don't know how to define "categorical_features" in SMOTENC. Besides, could you please let me know where to use imblearn.pipeline? Thanks in advance. Best regards,

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How to determine suitable cluster algo
by lampahome Jan. 26, 2019

Jan. 26, 2019

I want to do customized clustering algo for my datasets, that's cuz I don't want to try every algo and its hyperparameters. I though I just define the default range of import hyperparameters ex: number of cluster in K-means. I want to iterate some possible clutering alog like K-means, DBSCAN, AP...etc, and I choose the suitable algo to cluster for me. I'm not sure if that is able to do, but does GridSearchCV work for me? Or any other ways to determine that? thx

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