On 10 October 2016 at 12:22, Sean Violante <sean.viola...@gmail.com> wrote: > no ( but please check !) > > sample weights should be the counts for the respective label (0/1) > > [ I am actually puzzled about the glm help file - proportions loses how > often an input data 'row' was present relative to the other - though you > could do this by repeating the row 'n' times]
I think we might be talking at cross purposes. I have a matrix X where each row is a feature vector. I also have an array y where y[i] is a real number between 0 and 1. I would like to build a regression model that predicts the y values given the X rows. Now each y[i] value in fact comes from simply counting the number of positive labelled elements in a particular set (set i) and dividing by the number of elements in that set. So I can easily fit this into the model given by the R package glm by replacing each y[i] value by a pair of "Number of positives" and "Number of negatives" (this is case 2 in the docs I quoted) or using case 3 which asks for the y[i] plus the total number of elements in set i. I don't see how a single integer for sample_weight[i] would cover this information but I am sure I must have misunderstood. At best it seems it could cover the number of positive values but this is missing half the information. Raphael > > On Mon, Oct 10, 2016 at 1:15 PM, Raphael C <drr...@gmail.com> wrote: >> >> How do I use sample_weight for my use case? >> >> In my case is "y" an array of 0s and 1s and sample_weight then an >> array real numbers between 0 and 1 where I should make sure to set >> sample_weight[i]= 0 when y[i] = 0? >> >> Raphael >> >> On 10 October 2016 at 12:08, Sean Violante <sean.viola...@gmail.com> >> wrote: >> > should be the sample weight function in fit >> > >> > >> > http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html >> > >> > On Mon, Oct 10, 2016 at 1:03 PM, Raphael C <drr...@gmail.com> wrote: >> >> >> >> I just noticed this about the glm package in R. >> >> http://stats.stackexchange.com/a/26779/53128 >> >> >> >> " >> >> The glm function in R allows 3 ways to specify the formula for a >> >> logistic regression model. >> >> >> >> The most common is that each row of the data frame represents a single >> >> observation and the response variable is either 0 or 1 (or a factor >> >> with 2 levels, or other varibale with only 2 unique values). >> >> >> >> Another option is to use a 2 column matrix as the response variable >> >> with the first column being the counts of 'successes' and the second >> >> column being the counts of 'failures'. >> >> >> >> You can also specify the response as a proportion between 0 and 1, >> >> then specify another column as the 'weight' that gives the total >> >> number that the proportion is from (so a response of 0.3 and a weight >> >> of 10 is the same as 3 'successes' and 7 'failures')." >> >> >> >> Either of the last two options would do for me. Does scikit-learn >> >> support either of these last two options? >> >> >> >> Raphael >> >> >> >> On 10 October 2016 at 11:55, Raphael C <drr...@gmail.com> wrote: >> >> > I am trying to perform regression where my dependent variable is >> >> > constrained to be between 0 and 1. This constraint comes from the >> >> > fact >> >> > that it represents a count proportion. That is counts in some >> >> > category >> >> > divided by a total count. >> >> > >> >> > In the literature it seems that one common way to tackle this is to >> >> > use logistic regression. However, it appears that in scikit learn >> >> > logistic regression is only available as a classifier >> >> > >> >> > >> >> > (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html >> >> > ) . Is that right? >> >> > >> >> > Is there another way to perform regression using scikit learn where >> >> > the dependent variable is a count proportion? >> >> > >> >> > Thanks for any help. >> >> > >> >> > Raphael >> >> _______________________________________________ >> >> scikit-learn mailing list >> >> scikit-learn@python.org >> >> https://mail.python.org/mailman/listinfo/scikit-learn >> > >> > >> > >> > _______________________________________________ >> > scikit-learn mailing list >> > scikit-learn@python.org >> > https://mail.python.org/mailman/listinfo/scikit-learn >> > >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
