🌐 Mailman 3 December 2016 - scikit-learn - python.org

Bookmarklet to view documentation on CircleCI
by Joel Nothman April 7, 2017

April 7, 2017

At https://gist.github.com/jnothman/bf76d02f60af6476221ec65c63c77e60 I've created a bookmarklet which, when viewing a pull request page for which the CircleCI build has finished, will identify the circle build number and open a new tab with the changed documentation files corresponding to that PR.

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Your machine learning practical applications ?
by greg g Dec. 30, 2016

Dec. 30, 2016

Hi, Beginning with sklearn , i'm interested in practical applications of this library and applications of machine learning in general. <http://aka.ms/weboutlook>I would be grateful to hear 'real stories' about ML and eventually share them in an upcoming non-technical blog. Thanks if you can share some cases ... Gregory

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numpy.amin behaviour with multidimensionnal arrays
by greg g Dec. 29, 2016

Dec. 29, 2016

Hi, I would like to understand the behaviour of the scipy.spatial.kdtree<http://aka.ms/weboutlook> class that uses numpy.amin function. In the numpy.amin description, we find that it returns the "minimum value along a given axis" What does it mean exactly ? Thanks for any help Gregory

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Query Regarding Model Scoring using scikit learn's joblib library
by Debabrata Ghosh Dec. 29, 2016

Dec. 29, 2016

Dear All, Greetings! I need some urgent guidance and help from you all in model scoring. What I mean by model scoring is around the following steps: 1. I have trained a Random Classifier model using scikit-learn (RandomForestClassifier library) 2. Then I have generated the True Positive and False Positive predictions on my test data set using predict_proba method (I have splitted my data into training and test samples in 80:20 ratio) 3. Finally, I have dumped the model into a pkl file. 4. Next in another instance, I have loaded the .pkl file 5. I have initiated job_lib.predict_proba method for predicting the True Positive and False positives on a different sample. I am terming this step as scoring whether I am predicting without retraining the model My question is when I generate the True Positive Rate on the test data set (as part of model training approach), the rate which I am getting is 10 – 12%. But when I do the scoring (using the steps mentioned above), my True Positive Rate is shooting high upto 80%. Although, I am happy to get a very high TPR but my question is whether getting such a high TPR during the scoring phase is an expected outcome? In other words, whether achieving a high TPR through joblib is an accepted outcome vis-à-vis getting the TPR on training / test data set. Your views on the above ask will be really helpful as I am very confused whether to consider scoring the model using joblib. Otherwise is there any other alternative to joblib, which can help me to do scoring without retraining the model. Please let me know as per your earliest convenience as am a bit pressed Thanks for your help in advance! Cheers, Debu

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A math mistake in spectral_embedding
by gen tang Dec. 28, 2016

Dec. 28, 2016

Hi, everyone. I am quite new to this mail list. I think that I found a math mistake in spectral_embedding function. And I created a issue in github https://github.com/scikit-learn/scikit-learn/issues/8129 However, github can't show mathmatics equation, I send you pdf version(in attachment) of bug description by mail. Can anyone verify this math detail? Thanks a lot Cheers Gen

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combining arrays of features to train an MLP
by Thomas Evangelidis Dec. 20, 2016

Dec. 20, 2016

Greetings, My dataset consists of objects which are characterised by their structural features which are encoded into a so called "fingerprint" form. There are several different types of fingerprints, each one encapsulating different type of information. I want to combine two specific types of fingerprints to train a MLP regressor. The first fingerprint consists of a 2048 bit array of the form: > FP > 1 = array([ 1., 1., 0., ..., 0., 0., 1.], dtype=float32) The second is a 60 float number array of the form: FP2 = array([ 2.77494618, 0.98973243, 0.34638652, 2.88303715, > 1.31473857, > -0.56627112, 4.78847547, 2.29587913, -0.6786228 , 4.63391109, > ... > 0. , 0. , 5.89652792, 0. , 0. ]) At first I tried to fuse them into a single 1D array of 2048+60 columns but the predictions of the MLP were worse than the 2 different MLP models trained from one of the 2 fingerprint types individually. My question: is there a more effective way to combine the 2 fingerprints in order to indicate that they represent different type of information? To this end, I tried to create a 2-row array (1st row 2048 elements and 2nd row 60 elements) but sklearn complained: mlp.fit(x_train,y_train) > File > "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py", > line 618, in fit > return self._fit(X, y, incremental=False) > File > "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py", > line 330, in _fit > X, y = self._validate_input(X, y, incremental) > File > "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py", > line 1264, in _validate_input > multi_output=True, y_numeric=True) > File > "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line > 521, in check_X_y > ensure_min_features, warn_on_dtype, estimator) > File > "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line > 402, in check_array > array = array.astype(np.float64) > ValueError: setting an array element with a sequence. > Then I tried to create for each object of the dataset a 2D array of size 2x2048, by adding 1998 zeros in the second row in order both rows to be of equal size. However sklearn complained again: mlp.fit(x_train,y_train) > File > "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py", > line 618, in fit > return self._fit(X, y, incremental=False) > File > "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py", > line 330, in _fit > X, y = self._validate_input(X, y, incremental) > File > "/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py", > line 1264, in _validate_input > multi_output=True, y_numeric=True) > File > "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line > 521, in check_X_y > ensure_min_features, warn_on_dtype, estimator) > File > "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line > 405, in check_array > % (array.ndim, estimator_name)) > ValueError: Found array with dim 3. Estimator expected <= 2. In another case of fingerprints, lets name them FP3 and FP4, I observed that the MLP regressor created using FP3 yields better results when trained and evaluated using logarithmically transformed experimental values (the values in y_train and y_test 1D arrays), while the MLP regressor created using FP4 yielded better results using the original experimental values. So my second question is: when combining both FP3 and FP4 into a single array is there any way to designate to the MLP that the features that correspond to FP3 must reproduce the logarithmic transform of the experimental values while the features of FP4 the original untransformed experimental values? I would greatly appreciate any advice on any of my 2 queries. Thomas -- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 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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Failed when make PDF document by "make latexpdf"
by James Chang Dec. 20, 2016

Dec. 20, 2016

Hi, Does anyone have issue when execute "make latexpdf" to get PDF format Doc ? or I can directly download the latest PDF format doc for the currently stable version scikit-learn v 0.18.1 in some where? PS. I run the commend under Mac OS X 10.12.1 Thanks in advance and best regards, James

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n_jobs for LogisticRegression
by Sebastian Raschka Dec. 19, 2016

Dec. 19, 2016

Hi, I just got confused what exactly n_jobs does for LogisticRegression. Always thought that it was used for one-vs-rest learning, fitting the models for binary classification in parallel. However, it also seem to do sth in the multinomial case (at least according to the verbose option). in the docstring it says > n_jobs : int, optional > Number of CPU cores used during the cross-validation loop. If given > a value of -1, all cores are used. and I saw a logistic_regression_path being defined in the code. I am wondering, is this just a workaround for the LogisticRegressionCV, and should the n_jobs docstring in LogisticRegression be described as "Number of CPU cores used for model fitting” instead of “during cross-validation,” or am I getting this wrong? Best, Sebastian

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biased predictions in logistic regression
by Rachel Melamed Dec. 18, 2016

Dec. 18, 2016

Hi all, Does anyone have any suggestions for this problem: http://stackoverflow.com/questions/41125342/sklearn-logistic-regression-giv… I am running around 1000 similar logistic regressions, with the same covariates but slightly different data and response variables. All of my response variables have a sparse successes (p(success) < .05 usually). I noticed that with the regularized regression, the results are consistently biased to predict more "successes" than is observed in the training data. When I relax the regularization, this bias goes away. The bias observed is unacceptable for my use case, but the more-regularized model does seem a bit better. Below, I plot the results for the 1000 different regressions for 2 different values of C: [results for the different regressions for 2 different values of C] <https://i.stack.imgur.com/1cbrC.png> I looked at the parameter estimates for one of these regressions: below each point is one parameter. It seems like the intercept (the point on the bottom left) is too high for the C=1 model. [enter image description here] <https://i.stack.imgur.com/NTFOY.png>

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Model checksums
by Stuart Reynolds Dec. 15, 2016

Dec. 15, 2016

I'd like to cache some functions to avoid rebuilding models like so: @cached def train(model, dataparams): ... model is an (untrained) scikit-learn object and dataparams is a dict. The @cached annotation forms a SHA checksum out of the parameters of the function it annotates and returns the previously calculated function result if the parameters match. The tricky part here is reliably generating a checksum from the parameters. Scikit uses Python's pickle ( http://scikit-learn.org/stable/modules/model_persistence.html) but the pickle library is non-deterministic (same inputs to pickle.dumps yields differing output! -- *I know*). So... any suggestions on how to generate checksums from models in python? Thanks. - Stuart

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