[scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

Dear all, I posted the full question on StackOverflow and as it contains some figures I refer you to that post. https://stackoverflow.com/questions/44661926/sample- weight-parameter-shape-error-in-scikit-learn-gridsearchcv/44662285#44662285 I currently believe that this issue is a bug and I open

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

r a while now... -- Julio El 22 jun 2017, a las 23:33, Manuel Castejón Limas < manuel.caste...@gmail.com> escribió: Dear all, I posted the full question on StackOverflow and as it contains some figures I refer you to that post. https://stackoverflow.com/questions/44661926/sample-weight- parameter-sh

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

ng the latest version (or at least >0.17)? >> The code for splitting the sample weights in GridSearchCV has been there >> for a while now... >> >> -- >> Julio >> >> El 22 jun 2017, a las 23:33, Manuel Castejón Limas < >> manuel.caste...@gmail.co

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

ample_weight cannot be broadcast. I guess that the issue is that the sample__weight parameter was not thought to be changed during the tuning, was it? Thank you all for your patience and support. Best Manolo 2017-06-23 1:17 GMT+02:00 Manuel CASTEJÓN LIMAS : > Dear Joel, > I'm just pa

Re: [scikit-learn] Fwd: sample_weight parameter is not split when used in GridSearchCV

l Nothman escribió: yes, trying multiple sample weightings is not supported by grid search directly. On 23 Jun 2017 6:36 pm, "Manuel Castejón Limas" wrote: > Dear Joel, > > I tried and removed the square brackets and now it works as expected *for > a single* sam

[scikit-learn] Any plans on generalizing Pipeline and transformers?

, I'm stuck with this API limitation and I would love to learn some tricks from you if you could enlighten me. Thanks in advance! Manuel Castejón-Limas ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] Any plans on generalizing Pipeline and transformers?

nced datasets the > aforementioned objects may help your pipeline. > > Cheerz, > Chris > > On Tue, Dec 19, 2017 at 2:44 PM, Manuel Castejón Limas < > manuel.caste...@gmail.com> wrote: > >> Dear all, >> >> Kudos to scikit-learn! Having said that, Pip

Re: [scikit-learn] Any plans on generalizing Pipeline and transformers?

onSampler ( > https://github.com/scikit-learn-contrib/imbalanced-learn/pull/342 - we > are on the way to merge it) > > On 19 December 2017 at 13:44, Manuel Castejón Limas < > manuel.caste...@gmail.com> wrote: > >> Dear all, >> >> Kudos to scikit-learn! Having sai

Re: [scikit-learn] Any plans on generalizing Pipeline and transformers?

Thank you all for your interest! In order to clarify the case allow me to try to synthesize the spirit of what I'd like to put into the pipeline using this sequence of steps: #%% import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import DBSCAN from sklear

Re: [scikit-learn] Any plans on generalizing Pipeline and transformers?

I'm currently thinking on a computational graph which can then be wrapped as a pipeline like object ... I'll try yo make a toy example solving my problem. El 20 dic. 2017 16:33, "Manuel Castejón Limas" escribió: > Thank you all for your interest! > > In order to cla

Re: [scikit-learn] Any plans on generalizing Pipeline and transformers?

nt_name) return cg #%% cg = create_graph(graph_description) node_pos = {'First' : ( 0, 0), 'Concatenate_Xy' : ( 2, 4), 'Gaussian_Mixture' : ( 6, 8), 'Dbscan' : ( 6, 6), 'CombineClustering': ( 8, 7), 'Paell

Re: [scikit-learn] Any plans on generalizing Pipeline and transformers?

I've read about Dask and it is a tool I want to have in my belt especially for using the SGE connection in order to run GridSearchCV on the supercomputer center I have access to. Should it work as promised it will be one of my favs. As far as my toy example I keep more limited goals with this grap

Re: [scikit-learn] Any plans on generalizing Pipeline and transformers?

x27;), 'classification': ('Combine_Clustering', 'classification')}, 'use_for': ['fit'], }, 'Regressor': {'step': LinearRegression, 'kargs': {}, 'con

[scikit-learn] Pipegraph is on its way!

is also needed and we will provide a basic initial version in a later version. We need to write the documentation and we will propose it as a contrib-project in a few days. Best wishes, Manuel Castejón-Limas ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] clustering on big dataset

Hope this helps! Manuel @Article{Ciampi2008, author="Ciampi, Antonio and Lechevallier, Yves and Limas, Manuel Castej{\'o}n and Marcos, Ana Gonz{\'a}lez", title="Hierarchical clustering of subpopulations with a dissimilarity based on the likelihood ratio statistic: application to clustering

Re: [scikit-learn] Pipegraph is on its way!

Docs are coming soon. In the meantime , Imagine a first step containing a TrainTestSplit class with a similar behaviour to train_test_split but capable of producing results by using fit and predict (this is a goodie). The inputs will be X, y, z, ... , and the outputs the same names + _train and _t

[scikit-learn] PipeGraph examples: areas of interest

Hi all! The good news is that we made GridSearchCv work on PipeGraph! In order to create diverse examples, we welcome some feedback on which other libraries you use in order to acquire/process data before applying scikit learn. For example: 'I work in computer vision and I usually get image featu

Re: [scikit-learn] Pipegraph is on its way!

While we keep working on the docs and figures, here is a little example you all can already run: import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression from sklearn

[scikit-learn] PieGraph: First examples and documentation

Dear all, We have produced some documentation for the PipeGraph module. Essentially it consists of the API for the two main interfaces: PipeGraphRegressor and PipeGraphClassifier. I guess that at this point the best experience comes from reading the examples and watching the diagrams. These examp

Re: [scikit-learn] New Transformer

Dear David, We recently submitted PipeGraph as a sklearn contrib project. Even though it is an ongoing project and we are right now modifying the interface in order to make it more suitable and useful for the sklearn community, I believe that the problems that you explain can be addressed by PipeG

[scikit-learn] PipeGraph users guide

Dear all, we have written a users guide to PipeGraph in order to help the interested readers to better understand how it works. While we improve the rst export (the figures are missing) the best version is the original jupyter notebook: *https://github.com/mcasl/PipeGraph/blob/master/doc/User_Gui

[scikit-learn] Pipegraph feedback

sible scenarios not implemented yet by pipegraph, such as recurrent graphs, that might be potentially useful. Moreover, in case any core developer is interested in joining the project you are more than welcome! This would provide a great opportunity for collaboration! Best wishes Manuel Castejón-

Re: [scikit-learn] CircleCI

ng the > documentation. You could use a single CI service for all of those. > However, I am not sure that you have Windows support apart of > Appveyor. > > I think that we should update the template of the scikit-learn-contrib > with the new template for circle ci 2. > > Cheers,

[scikit-learn] Pipegraph example: KMeans + LDA

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/au

Re: [scikit-learn] Pipegraph example: KMeans + LDA

The long story short: Thank you for your time & sorry for inaccuracies; a few words selling a modular approach to your developments; and a request on your opinion on parallelizing Pipegraph using dask. Thank you Andreas for your patience showing me the sklearn ways. I admit that I'm still learning

Re: [scikit-learn] Delegating "get_params" and "set_params" to a wrapped estimator when parameter is not defined.

Hi Javier! Yo can have a look at: https://github.com/mcasl/PipeGraph/blob/master/pipegraph/adapters.py There are a few adapters there and I had tool deal with that situation. I solved it by using __getattr__ and __setattr__. Best Manolo El vie., 13 abr. 2018 17:53, Javier López escribió: > I h

Re: [scikit-learn] Delegating "get_params" and "set_params" to a wrapped estimator when parameter is not defined.

will have better grounds to make an educated decision. Best Manuel Manuel Castejón Limas *Escuela de Ingeniería Industrial e Informática* Universidad de León Campus de Vegazana sn. 24071. León. Spain. *e-mail: *manuel.caste...@unileon.es *Tel.*: +34 987 291 946 Digital Business Card: Click Here <

Re: [scikit-learn] Delegating "get_params" and "set_params" to a wrapped estimator when parameter is not defined.

l make the necessary changes to the docs and then the master branch will be replaced with this new Mixin classes version. Thanks for pointing out this issue! Best Manuel 2018-04-16 14:21 GMT+02:00 Manuel CASTEJÓN LIMAS : > Nope! Mostly because of lack of experience with mixins. > I've done

[scikit-learn] CircleCI

Dear all, Contrib projects template hints the authors to use TravisCI, CircleCI and Appveyor. Now that CircleCI has moved to version 2, is there any idea on what to do about it? Will the template be updated? Is it ok if we use only CircleCI? What do you, core devs, suggest about that? Best wishes M

Re: [scikit-learn] [ANN] Scikit-learn 0.20.0

Huge huge Thank you developers! Keep up the good work! El mié., 26 sept. 2018 20:57, Andreas Mueller escribió: > Hey everbody! > I'm happy to (finally) announce scikit-learn 0.20.0. > This release is dedicated to the memory of Raghav Rajagopalan. > > You can upgrade now with pip or conda! > > Th

Re: [scikit-learn] [ANN] Scikit-learn 0.20.0

How about a docker based approach? Just thinking out loud Best Manuel El vie., 28 sept. 2018 19:43, Andreas Mueller escribió: > > > On 09/28/2018 01:38 PM, Andreas Mueller wrote: > > > > > > On 09/28/2018 12:10 PM, Sebastian Raschka wrote: > I think model serialization should be a priority.

Re: [scikit-learn] scikit-learn Digest, Vol 30, Issue 25

I would propose PipeGraph for stacking, it comes natural and it could help a lot in making things easier for core developers. Disclaimer: I'm coauthor of PipeGraph Manuel Castejón Limas Escuela de Ingenierías Industrial, Informática y Aeroespacial Universidad de León Campus de Vegaza

Re: [scikit-learn] scikit-learn Digest, Vol 30, Issue 25

Good to know! El lun., 8 oct. 2018 9:08, Joel Nothman escribió: > Just a note that multiple layers of stacking can be achieved with > StackingClassifier using nesting. > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.or

Re: [scikit-learn] baggingClassifier with pipeline

You can always add a first step that turns you numpy array into a DataFrame such as the one required afterwards. A bit of object oriented programming might be required though, for deriving you class from BaseTransformer and writing you particular code for fit and transform method. Alternatively you