[Scikit-learn-general] PEP8 alert!
PEP8 violations are reaching a critical level causing a risk of code style meltdown: https://jenkins.shiningpanda-ci.com/scikit-learn/job/python-2.7-numpy-1.6.2-scipy-0.10.1/violations/ We should be more careful in checking pep8 compliance prior to merging PRs from now on :) And remember kids: nobody expects the PEP8 inquisition. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Google Summer of Code 2014
Thanks for your responses. @Kyle: At the risk of sounding really naive, I'd like to make the following comments. I'm referring to this paper that Sukru had posted, http://www.stat.osu.edu/~dmsl/Sarwar_2001.pdf which is item based collaborative filtering. I don't think there is really any need for masking the items that are not selected by the target user (or the user for which you need to predict the item rating) here. I believe it would work for dense cases too. Lets look at a sample session here. from sklearn.recsys import item_cf # Tentative names. clf = item_cf() # Here arguments like similarity criteria, number of recommendations can be given in the __init__ # Lets say there are n users who have have already rated, # X is an 2-D array with the first dimension of n, the second can vary according to the number of items they have # rated. # y is the ratings they have provided. This can be either binary like +1 or -1 , or continuous. clf.fit(X, y) # After doing clf.fit(X, y) , an attribute clf.items_ would return the total number of items. clf.predict(x) # This will return the top n recommendations of x # For each item in clf.items_ provided item is not in x, similarity is calculated by taking the top k similar items in x. For user based CF, yes we need to provide a mask for the item for which we need to predict the rating, but I suppose that can be provided in the __init__ (can't it)? @Alex and Nick: Thanks for your references, I'll have a look right now. However a point I don't intutively understand what clf.transform() / clf.fit_transform must be doing in these cases. Any pointers? Considering the mentor problem, I don't think that would be a problem if the community is genuinely interested in this project. If I do get a +1, I can start thinking about the timeline, algorithms I'd like to implement etc. I'm really looking forward to extending my really minor scikit-learn work right now as part of GSoC. -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Google Summer of Code 2014
So X is the array of existing ratings, would y be a 2D array then? If not, how do you map the ratings given back to a single user (since y is typically, to my knowledge, 1D in sklearn)? I am still a little confused, but your example helped. Can you could go into a little more detail on X, x, and y? Let's say for an example of 5 users, 11 total items. That would make X a 5x11 matrix, right? What about y and x? On Thu, Jan 16, 2014 at 8:29 AM, Manoj Kumar manojkumarsivaraj...@gmail.com wrote: Thanks for your responses. @Kyle: At the risk of sounding really naive, I'd like to make the following comments. I'm referring to this paper that Sukru had posted, http://www.stat.osu.edu/~dmsl/Sarwar_2001.pdf which is item based collaborative filtering. I don't think there is really any need for masking the items that are not selected by the target user (or the user for which you need to predict the item rating) here. I believe it would work for dense cases too. Lets look at a sample session here. from sklearn.recsys import item_cf # Tentative names. clf = item_cf() # Here arguments like similarity criteria, number of recommendations can be given in the __init__ # Lets say there are n users who have have already rated, # X is an 2-D array with the first dimension of n, the second can vary according to the number of items they have # rated. # y is the ratings they have provided. This can be either binary like +1 or -1 , or continuous. clf.fit(X, y) # After doing clf.fit(X, y) , an attribute clf.items_ would return the total number of items. clf.predict(x) # This will return the top n recommendations of x # For each item in clf.items_ provided item is not in x, similarity is calculated by taking the top k similar items in x. For user based CF, yes we need to provide a mask for the item for which we need to predict the rating, but I suppose that can be provided in the __init__ (can't it)? @Alex and Nick: Thanks for your references, I'll have a look right now. However a point I don't intutively understand what clf.transform() / clf.fit_transform must be doing in these cases. Any pointers? Considering the mentor problem, I don't think that would be a problem if the community is genuinely interested in this project. If I do get a +1, I can start thinking about the timeline, algorithms I'd like to implement etc. I'm really looking forward to extending my really minor scikit-learn work right now as part of GSoC. -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Google Summer of Code 2014
Well y can be 2-D too, there are estimators like MultiTaskElasticNet especially meant for multi-task y. I was thinking something along these lines. Lets say [ham, spam, ram, bam, tam] are the five items. and if first user gives ham - 2 spam - 3 the second user gives ram - 1 bam - -3 tam - 4 then I was thinking X = [[ham, spam], [ram, bam, tam]] and y = [[2, 3], []] On Thu, Jan 16, 2014 at 8:56 PM, Kyle Kastner kastnerk...@gmail.com wrote: So X is the array of existing ratings, would y be a 2D array then? If not, how do you map the ratings given back to a single user (since y is typically, to my knowledge, 1D in sklearn)? I am still a little confused, but your example helped. Can you could go into a little more detail on X, x, and y? Let's say for an example of 5 users, 11 total items. That would make X a 5x11 matrix, right? What about y and x? On Thu, Jan 16, 2014 at 8:29 AM, Manoj Kumar manojkumarsivaraj...@gmail.com wrote: Thanks for your responses. @Kyle: At the risk of sounding really naive, I'd like to make the following comments. I'm referring to this paper that Sukru had posted, http://www.stat.osu.edu/~dmsl/Sarwar_2001.pdf which is item based collaborative filtering. I don't think there is really any need for masking the items that are not selected by the target user (or the user for which you need to predict the item rating) here. I believe it would work for dense cases too. Lets look at a sample session here. from sklearn.recsys import item_cf # Tentative names. clf = item_cf() # Here arguments like similarity criteria, number of recommendations can be given in the __init__ # Lets say there are n users who have have already rated, # X is an 2-D array with the first dimension of n, the second can vary according to the number of items they have # rated. # y is the ratings they have provided. This can be either binary like +1 or -1 , or continuous. clf.fit(X, y) # After doing clf.fit(X, y) , an attribute clf.items_ would return the total number of items. clf.predict(x) # This will return the top n recommendations of x # For each item in clf.items_ provided item is not in x, similarity is calculated by taking the top k similar items in x. For user based CF, yes we need to provide a mask for the item for which we need to predict the rating, but I suppose that can be provided in the __init__ (can't it)? @Alex and Nick: Thanks for your references, I'll have a look right now. However a point I don't intutively understand what clf.transform() / clf.fit_transform must be doing in these cases. Any pointers? Considering the mentor problem, I don't think that would be a problem if the community is genuinely interested in this project. If I do get a +1, I can start thinking about the timeline, algorithms I'd like to implement etc. I'm really looking forward to extending my really minor scikit-learn work right now as part of GSoC. -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Regards, Manoj Kumar, Mech Undergrad http://manojbits.wordpress.com -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Google Summer of Code 2014
I'm extremely sorry, that message got sent half way through. (I pressed Ctrl + Enter by mistake) X = [[ham, spam], [ram, bam, tam]], and y = [[2, 3], [1, -3, 4]] and we do clf.fit(X, y) Suppose we would like to predict, what we would recommend the user x who has already rated ram as 1 and bam as 2. we do clf.predict([ram, bam], [1, -3]) and it would give the output. (Both parameters are required) I do not know however what clf.transform() or clf.fit_transform() would do (as of know), and the meat of the computation would be done in clf.predict() -- Regards, Manoj Kumar, Mech Undergrad http://manojbits.wordpress.com -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
[Scikit-learn-general] Suggestion to add author names/emails at the bottom of module documentations
Hi scikit-learn editors, Any documentation can have mistakes, but it's important to address them quickly and efficiently. One plausible way is to contact the author of an erroneous text to have him make proper changes. But, for all I know, scikit-learn's documentation lacks authors' information; which could otherwise help users or experts in the field to readily request to address false information. Clearly, they can address such issues through scikit's mailing list, but not many people are willing to take that extra mile of looking for the mailing list address, waiting for approval, and keeping track of the mailing list posts, especially professors who are rather busy with their research work. How about if one opens a PR to add the corresponding author's information at the bottom of each module 's documentation (just like in newspapers) ? Thank you! :) :) -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Google Summer of Code 2014
I agree that sparse matrices need to be supported as one of the main properties inherent to the user/item rating matrix in recommender systems is its sparsity. This sparsity is what has given rise to such a large scale of research in the field. Hence this property would have to be taken advantage of because if not, since we have to deal with matrices, similarity calculations would have complexity through the roof (although there are ways to overcome this by using item-item cf techniques where similarity calculation is done offline but nevertheless is still expensive). Possibly solutions in my opinion: 1 Support dense and sparse matrices but I am not sure if such an implementation can be directly plugged into sklearn (because of the sparse matrix support.) 2 Distributed recommender systems (just provide the ability for people to distribute their similarity calculations.) This can be done using MRJob a hadoop-streaming wrapper for python. This is also a current field of research and I'm sure if you look into it you will find quite a lot of literature on the topic. 3 I am currently also trying to look into this library called scikit-crab which was started based upon a similar plan but I heard the developers are rewriting the library currently and it might not be open to the community for active development at present (not sure about this though). But I just mentioned it thinking maybe if you took a look at the code, you would get some more ideas about what improvements could be made. https://github.com/muricoca/crab From: Kyle Kastner [kastnerk...@gmail.com] Sent: Wednesday, January 15, 2014 1:42 PM To: scikit-learn-general@lists.sourceforge.net Subject: Re: [Scikit-learn-general] Google Summer of Code 2014 I looked into this once upon a time, and one of the key problems (from talking to Jake IIRC) is how to handle the missing values in the input array. You would either need a mask, or some kind of indexing system for describing which value goes where in the input matrix. Either way, this extra argument would be a requirement for CF, but not for the existing algorithms in sklearn. Maybe it would only operate on sparse arrays, and infer that the values which are missing are the ones to be imputed (recommended)? But not supporting dense arrays would basically be the opposite of other modules in sklearn, where dense input is default. Maybe someone can comment on this? I don't know how well this lines up with the existing API/functionality and the future directions there, but how to deal with the missing values is probably the primary concern for implementing CF algorithms in sklearn IMO. On Wed, Jan 15, 2014 at 12:07 PM, Manoj Kumar manojkumarsivaraj...@gmail.commailto:manojkumarsivaraj...@gmail.com wrote: Hello, First of all, thanks to the scikit-learn community for guiding new developers. I'm thankful for all the help that I've got with my Pull Requests till now. I hope that this is the right place to discuss GSoC related ideas (I've idled at the scikit-learn irc channel for quite a few occasions, but I could not meet any core developer). I was browsing through the threads of last year, when I found this idea related to collaborative filtering (CF) quite interesting, http://sourceforge.net/mailarchive/message.php?msg_id=30725712 , though this was sadly not accepted. If the scikit-learn community is still enthusiastic about a recsys module with CF algorithms implemented, I would love this to be my GSoC proposal and we could discuss more about the algorithms, gelling with the present sklearn API, how much we could possibly fit in a 3 month period etc. Awaiting a reply. -- Regards, Manoj Kumar, Mech Undergrad http://manojbits.wordpress.com -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.netmailto:Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Suggestion to add author names/emails at the bottom of module documentations
I would rather have this sorted out through the github issue tracker. I don't think it's a good idea to encourage users to e-mail individual developers. Someone else could have the expertise and do the change confidently. My 2c, Vlad On Thu Jan 16 18:12:05 2014, Issam wrote: Hi scikit-learn editors, Any documentation can have mistakes, but it's important to address them quickly and efficiently. One plausible way is to contact the author of an erroneous text to have him make proper changes. But, for all I know, scikit-learn's documentation lacks authors' information; which could otherwise help users or experts in the field to readily request to address false information. Clearly, they can address such issues through scikit's mailing list, but not many people are willing to take that extra mile of looking for the mailing list address, waiting for approval, and keeping track of the mailing list posts, especially professors who are rather busy with their research work. How about if one opens a PR to add the corresponding author's information at the bottom of each module 's documentation (just like in newspapers) ? Thank you! :) :) -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Google Summer of Code 2014
@Manoj The predict stage taking 2 parameters is what I was talking about - are there any other estimators that need anything more than a single matrix to do a prediction? I do not recall any - this would be something particular to CF. Maybe you could recast it as a matrix with alternating rows of item,rating but that is still a particular for CF. Whether that is OK as far sklearn's API is concerned is not for me to decide. I would also expect it to be closely tied with DictVectorizer or something like it, probably more so than most other algorithms (though this is not a big deal IMO) to get categorical labels. @nmuralid I agree totally - last number I saw was that the typical matrix for something like Amazon is 99% sparse? I don't remember where I read it though. Looking at crab, it seems like they are trying to do sklearn-style API specifically for collaborative filtering. Not sure where the name crab comes in, but it is definitely worth looking at. Kyle On Thu, Jan 16, 2014 at 11:17 AM, nmura...@masonlive.gmu.edu nmura...@masonlive.gmu.edu wrote: I agree that sparse matrices need to be supported as one of the main properties inherent to the user/item rating matrix in recommender systems is its sparsity. This sparsity is what has given rise to such a large scale of research in the field. Hence this property would have to be taken advantage of because if not, since we have to deal with matrices, similarity calculations would have complexity through the roof (although there are ways to overcome this by using item-item cf techniques where similarity calculation is done offline but nevertheless is still expensive). Possibly solutions in my opinion: 1 Support dense and sparse matrices but I am not sure if such an implementation can be directly plugged into sklearn (because of the sparse matrix support.) 2 Distributed recommender systems (just provide the ability for people to distribute their similarity calculations.) This can be done using MRJob a hadoop-streaming wrapper for python. This is also a current field of research and I'm sure if you look into it you will find quite a lot of literature on the topic. 3 I am currently also trying to look into this library called scikit-crab which was started based upon a similar plan but I heard the developers are rewriting the library currently and it might not be open to the community for active development at present (not sure about this though). But I just mentioned it thinking maybe if you took a look at the code, you would get some more ideas about what improvements could be made. https://github.com/muricoca/crab -- *From:* Kyle Kastner [kastnerk...@gmail.com] *Sent:* Wednesday, January 15, 2014 1:42 PM *To:* scikit-learn-general@lists.sourceforge.net *Subject:* Re: [Scikit-learn-general] Google Summer of Code 2014 I looked into this once upon a time, and one of the key problems (from talking to Jake IIRC) is how to handle the missing values in the input array. You would either need a mask, or some kind of indexing system for describing which value goes where in the input matrix. Either way, this extra argument would be a requirement for CF, but not for the existing algorithms in sklearn. Maybe it would only operate on sparse arrays, and infer that the values which are missing are the ones to be imputed (recommended)? But not supporting dense arrays would basically be the opposite of other modules in sklearn, where dense input is default. Maybe someone can comment on this? I don't know how well this lines up with the existing API/functionality and the future directions there, but how to deal with the missing values is probably the primary concern for implementing CF algorithms in sklearn IMO. On Wed, Jan 15, 2014 at 12:07 PM, Manoj Kumar manojkumarsivaraj...@gmail.com wrote: Hello, First of all, thanks to the scikit-learn community for guiding new developers. I'm thankful for all the help that I've got with my Pull Requests till now. I hope that this is the right place to discuss GSoC related ideas (I've idled at the scikit-learn irc channel for quite a few occasions, but I could not meet any core developer). I was browsing through the threads of last year, when I found this idea related to collaborative filtering (CF) quite interesting, http://sourceforge.net/mailarchive/message.php?msg_id=30725712 , though this was sadly not accepted. If the scikit-learn community is still enthusiastic about a recsys module with CF algorithms implemented, I would love this to be my GSoC proposal and we could discuss more about the algorithms, gelling with the present sklearn API, how much we could possibly fit in a 3 month period etc. Awaiting a reply. -- Regards, Manoj Kumar, Mech Undergrad http://manojbits.wordpress.com -- CenturyLink Cloud: The Leader in
Re: [Scikit-learn-general] Google Summer of Code 2014
Yes indeed, getting two parameters for predict would be specific to CF. That was the most obvious idea that came to my mind. I would like to hear other's opinions also regarding the API, and the feasibility of such a project. On Thu, Jan 16, 2014 at 11:47 PM, Kyle Kastner kastnerk...@gmail.comwrote: @Manoj The predict stage taking 2 parameters is what I was talking about - are there any other estimators that need anything more than a single matrix to do a prediction? I do not recall any - this would be something particular to CF. Maybe you could recast it as a matrix with alternating rows of item,rating but that is still a particular for CF. Whether that is OK as far sklearn's API is concerned is not for me to decide. I would also expect it to be closely tied with DictVectorizer or something like it, probably more so than most other algorithms (though this is not a big deal IMO) to get categorical labels. @nmuralid I agree totally - last number I saw was that the typical matrix for something like Amazon is 99% sparse? I don't remember where I read it though. Looking at crab, it seems like they are trying to do sklearn-style API specifically for collaborative filtering. Not sure where the name crab comes in, but it is definitely worth looking at. Kyle On Thu, Jan 16, 2014 at 11:17 AM, nmura...@masonlive.gmu.edu nmura...@masonlive.gmu.edu wrote: I agree that sparse matrices need to be supported as one of the main properties inherent to the user/item rating matrix in recommender systems is its sparsity. This sparsity is what has given rise to such a large scale of research in the field. Hence this property would have to be taken advantage of because if not, since we have to deal with matrices, similarity calculations would have complexity through the roof (although there are ways to overcome this by using item-item cf techniques where similarity calculation is done offline but nevertheless is still expensive). Possibly solutions in my opinion: 1 Support dense and sparse matrices but I am not sure if such an implementation can be directly plugged into sklearn (because of the sparse matrix support.) 2 Distributed recommender systems (just provide the ability for people to distribute their similarity calculations.) This can be done using MRJob a hadoop-streaming wrapper for python. This is also a current field of research and I'm sure if you look into it you will find quite a lot of literature on the topic. 3 I am currently also trying to look into this library called scikit-crab which was started based upon a similar plan but I heard the developers are rewriting the library currently and it might not be open to the community for active development at present (not sure about this though). But I just mentioned it thinking maybe if you took a look at the code, you would get some more ideas about what improvements could be made. https://github.com/muricoca/crab -- *From:* Kyle Kastner [kastnerk...@gmail.com] *Sent:* Wednesday, January 15, 2014 1:42 PM *To:* scikit-learn-general@lists.sourceforge.net *Subject:* Re: [Scikit-learn-general] Google Summer of Code 2014 I looked into this once upon a time, and one of the key problems (from talking to Jake IIRC) is how to handle the missing values in the input array. You would either need a mask, or some kind of indexing system for describing which value goes where in the input matrix. Either way, this extra argument would be a requirement for CF, but not for the existing algorithms in sklearn. Maybe it would only operate on sparse arrays, and infer that the values which are missing are the ones to be imputed (recommended)? But not supporting dense arrays would basically be the opposite of other modules in sklearn, where dense input is default. Maybe someone can comment on this? I don't know how well this lines up with the existing API/functionality and the future directions there, but how to deal with the missing values is probably the primary concern for implementing CF algorithms in sklearn IMO. On Wed, Jan 15, 2014 at 12:07 PM, Manoj Kumar manojkumarsivaraj...@gmail.com wrote: Hello, First of all, thanks to the scikit-learn community for guiding new developers. I'm thankful for all the help that I've got with my Pull Requests till now. I hope that this is the right place to discuss GSoC related ideas (I've idled at the scikit-learn irc channel for quite a few occasions, but I could not meet any core developer). I was browsing through the threads of last year, when I found this idea related to collaborative filtering (CF) quite interesting, http://sourceforge.net/mailarchive/message.php?msg_id=30725712 , though this was sadly not accepted. If the scikit-learn community is still enthusiastic about a recsys module with CF algorithms implemented, I would love this to be my GSoC proposal and we could discuss more about the
Re: [Scikit-learn-general] Suggestion to add author names/emails at the bottom of module documentations
I agree with Vlad. Further, if there is documentation or a module that none of the active developers can touch (due to complexity or lack of expertise), the preference has generally been to move to remove it from scikit-learn. On 17 January 2014 05:12, Vlad Niculae zephy...@gmail.com wrote: I would rather have this sorted out through the github issue tracker. I don't think it's a good idea to encourage users to e-mail individual developers. Someone else could have the expertise and do the change confidently. My 2c, Vlad On Thu Jan 16 18:12:05 2014, Issam wrote: Hi scikit-learn editors, Any documentation can have mistakes, but it's important to address them quickly and efficiently. One plausible way is to contact the author of an erroneous text to have him make proper changes. But, for all I know, scikit-learn's documentation lacks authors' information; which could otherwise help users or experts in the field to readily request to address false information. Clearly, they can address such issues through scikit's mailing list, but not many people are willing to take that extra mile of looking for the mailing list address, waiting for approval, and keeping track of the mailing list posts, especially professors who are rather busy with their research work. How about if one opens a PR to add the corresponding author's information at the bottom of each module 's documentation (just like in newspapers) ? Thank you! :) :) -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Public key at: http://pgp.mit.edu/ Search for this email address and select the key from 2011-08-19 (key id: 54BA8735) -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Google Summer of Code 2014
The other thing to keep mind an ideal solution would be compatible with Pipeline() - it would be nice to be able to use it there, which is one of the reasons a different signature for the predict() method is an issue. Hopefully something can be figured out, as there is a lot interest in CF algorithms, and a large majority of the algorithmic work (at least for the CF algorithm I looked at) is already present in the NMF code. On Thu, Jan 16, 2014 at 1:09 PM, Manoj Kumar manojkumarsivaraj...@gmail.com wrote: Yes indeed, getting two parameters for predict would be specific to CF. That was the most obvious idea that came to my mind. I would like to hear other's opinions also regarding the API, and the feasibility of such a project. On Thu, Jan 16, 2014 at 11:47 PM, Kyle Kastner kastnerk...@gmail.comwrote: @Manoj The predict stage taking 2 parameters is what I was talking about - are there any other estimators that need anything more than a single matrix to do a prediction? I do not recall any - this would be something particular to CF. Maybe you could recast it as a matrix with alternating rows of item,rating but that is still a particular for CF. Whether that is OK as far sklearn's API is concerned is not for me to decide. I would also expect it to be closely tied with DictVectorizer or something like it, probably more so than most other algorithms (though this is not a big deal IMO) to get categorical labels. @nmuralid I agree totally - last number I saw was that the typical matrix for something like Amazon is 99% sparse? I don't remember where I read it though. Looking at crab, it seems like they are trying to do sklearn-style API specifically for collaborative filtering. Not sure where the name crab comes in, but it is definitely worth looking at. Kyle On Thu, Jan 16, 2014 at 11:17 AM, nmura...@masonlive.gmu.edu nmura...@masonlive.gmu.edu wrote: I agree that sparse matrices need to be supported as one of the main properties inherent to the user/item rating matrix in recommender systems is its sparsity. This sparsity is what has given rise to such a large scale of research in the field. Hence this property would have to be taken advantage of because if not, since we have to deal with matrices, similarity calculations would have complexity through the roof (although there are ways to overcome this by using item-item cf techniques where similarity calculation is done offline but nevertheless is still expensive). Possibly solutions in my opinion: 1 Support dense and sparse matrices but I am not sure if such an implementation can be directly plugged into sklearn (because of the sparse matrix support.) 2 Distributed recommender systems (just provide the ability for people to distribute their similarity calculations.) This can be done using MRJob a hadoop-streaming wrapper for python. This is also a current field of research and I'm sure if you look into it you will find quite a lot of literature on the topic. 3 I am currently also trying to look into this library called scikit-crab which was started based upon a similar plan but I heard the developers are rewriting the library currently and it might not be open to the community for active development at present (not sure about this though). But I just mentioned it thinking maybe if you took a look at the code, you would get some more ideas about what improvements could be made. https://github.com/muricoca/crab -- *From:* Kyle Kastner [kastnerk...@gmail.com] *Sent:* Wednesday, January 15, 2014 1:42 PM *To:* scikit-learn-general@lists.sourceforge.net *Subject:* Re: [Scikit-learn-general] Google Summer of Code 2014 I looked into this once upon a time, and one of the key problems (from talking to Jake IIRC) is how to handle the missing values in the input array. You would either need a mask, or some kind of indexing system for describing which value goes where in the input matrix. Either way, this extra argument would be a requirement for CF, but not for the existing algorithms in sklearn. Maybe it would only operate on sparse arrays, and infer that the values which are missing are the ones to be imputed (recommended)? But not supporting dense arrays would basically be the opposite of other modules in sklearn, where dense input is default. Maybe someone can comment on this? I don't know how well this lines up with the existing API/functionality and the future directions there, but how to deal with the missing values is probably the primary concern for implementing CF algorithms in sklearn IMO. On Wed, Jan 15, 2014 at 12:07 PM, Manoj Kumar manojkumarsivaraj...@gmail.com wrote: Hello, First of all, thanks to the scikit-learn community for guiding new developers. I'm thankful for all the help that I've got with my Pull Requests till now. I hope that this is the right place to discuss GSoC related ideas (I've idled at the
Re: [Scikit-learn-general] Google Summer of Code 2014
`y` is by definition hidden at prediction time for supervised learning, so
I don't think your representation makes sense. But I see this as a
completion problem, not a supervised learning problem: the same data is
observed at training and predict time.
Isn't the following:
X = [[ham, spam], [ram, bam, tam]], and y = [[2, 3], [1, -3, 4]]
equivalent to [{'ham': 2, 'spam': 3}, {'ram': 2, 'bam': -3, 'tam': 4}]?
Via DictVectorizer, this becomes equivalent to a sparse COO matrix with:
col = [0, 1, 2, 3, 4]
row = [0, 0, 1, 1, 1]
data = [2, 3, 2, -3, 4]
As far as I can tell, this is a plain old sparse matrix, without a need for
an extra `y`. (Please convince me otherwise!)
There are still issues of whether this is in scikit-learn scope. For
example, does it make sense with sklearn's cross validation? Or will you
want to cross validate on both axes? Given that there is plenty of work to
be done that is well within scikit-learn's scope (prominent alternative
solutions and utilities for problems it already solves), I think this
extension of scope needs to be argued.
On 17 January 2014 09:24, Kyle Kastner kastnerk...@gmail.com wrote:
The other thing to keep mind an ideal solution would be compatible with
Pipeline() - it would be nice to be able to use it there, which is one of
the reasons a different signature for the predict() method is an issue.
Hopefully something can be figured out, as there is a lot interest in CF
algorithms, and a large majority of the algorithmic work (at least for the
CF algorithm I looked at) is already present in the NMF code.
On Thu, Jan 16, 2014 at 1:09 PM, Manoj Kumar
manojkumarsivaraj...@gmail.com wrote:
Yes indeed, getting two parameters for predict would be specific to CF.
That was the most obvious idea that came to my mind. I would like to hear
other's opinions also regarding the API, and the feasibility of such a
project.
On Thu, Jan 16, 2014 at 11:47 PM, Kyle Kastner kastnerk...@gmail.comwrote:
@Manoj
The predict stage taking 2 parameters is what I was talking about - are
there any other estimators that need anything more than a single matrix to
do a prediction? I do not recall any - this would be something particular
to CF. Maybe you could recast it as a matrix with alternating rows of
item,rating but that is still a particular for CF.
Whether that is OK as far sklearn's API is concerned is not for me to
decide. I would also expect it to be closely tied with DictVectorizer or
something like it, probably more so than most other algorithms (though this
is not a big deal IMO) to get categorical labels.
@nmuralid
I agree totally - last number I saw was that the typical matrix for
something like Amazon is 99% sparse? I don't remember where I read it
though. Looking at crab, it seems like they are trying to do sklearn-style
API specifically for collaborative filtering. Not sure where the name crab
comes in, but it is definitely worth looking at.
Kyle
On Thu, Jan 16, 2014 at 11:17 AM, nmura...@masonlive.gmu.edu
nmura...@masonlive.gmu.edu wrote:
I agree that sparse matrices need to be supported as one of the main
properties inherent to the user/item rating matrix in recommender systems
is its sparsity. This sparsity is what has given rise to such a large scale
of research in the field. Hence this property would have to be taken
advantage of because if not, since we have to deal with matrices,
similarity calculations would have complexity through the roof (although
there are ways to overcome this by using item-item cf techniques where
similarity calculation is done offline but nevertheless is still
expensive).
Possibly solutions in my opinion:
1 Support dense and sparse matrices but I am not sure if such an
implementation can be directly plugged into sklearn (because of the sparse
matrix support.)
2 Distributed recommender systems (just provide the ability for
people to distribute their similarity calculations.) This can be done using
MRJob a hadoop-streaming wrapper for python. This is also a current field
of research and I'm sure if you look into it you will find quite a lot of
literature on the topic.
3 I am currently also trying to look into this library called
scikit-crab which was started based upon a similar plan but I heard the
developers are rewriting the library currently and it might not be open to
the community for active development at present (not sure about this
though). But I just mentioned it thinking maybe if you took a look at the
code, you would get some more ideas about what improvements could be made.
https://github.com/muricoca/crab
--
*From:* Kyle Kastner [kastnerk...@gmail.com]
*Sent:* Wednesday, January 15, 2014 1:42 PM
*To:* scikit-learn-general@lists.sourceforge.net
*Subject:* Re: [Scikit-learn-general] Google Summer of Code 2014
I looked into this once upon a time, and one of the key problems
(from talking to Jake IIRC) is how to
Re: [Scikit-learn-general] Google Summer of Code 2014
2014/1/16 Joel Nothman joel.noth...@gmail.com: There are still issues of whether this is in scikit-learn scope. For example, does it make sense with sklearn's cross validation? Or will you want to cross validate on both axes? Given that there is plenty of work to be done that is well within scikit-learn's scope (prominent alternative solutions and utilities for problems it already solves), I think this extension of scope needs to be argued. +1 I would first focus on generic matrix factorization / completion estimators as unsupervised estimators (using the standard model.fit(X) API with a scipy sparse X). Then real a CF system could leverage such building blocks to build its features in 3rd party libraries that would build upon scikit-learn but would provide the domain specific recsys boilerplate. -- Olivier -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
[Scikit-learn-general] Automated scikit-learn dev documentation build
Hi all, Jaques and I have recently been working on moving the dev documentation build job out of Fabian's workstation to a server on the public Rackspace Cloud. The deployment of the documentation build server is now fully automated thanks to this script and configuration: https://github.com/scikit-learn/sklearn-docbuilder It uses Apache Libcloud to contact Rackspace, find the IP address of the docbuilder server or start a Ubuntu 12.04 VM if no existing VM can be found then uses SaltStack to configure the server: - create a non-root user sklearn - install a pair of ssh keys suitable for sourceforge upload - install all build dependencies using system packages - create a venv to install additional python packages (sphinx) in more recent versions - clone the scikit-learn git repo - configure a cron job that updates the git repo, build sklearn, build the doc and rsync it to sourceforge every hour. The README.md has more info. If you are curious the salt configuration is here: https://github.com/scikit-learn/sklearn-docbuilder/tree/master/srv/salt The provisioning script is here: https://github.com/scikit-learn/sklearn-docbuilder/blob/master/docbuilder.py If you are interested in being a co-admin for this, please feel free to ask me the rackspace credentials and the private SSH key. Thanks again to rackspace for the free Rackspace Cloud account. I will issue a pull request to thank them in the doc. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Google Summer of Code 2014
Thanks everyone for your quick responses. 1. Could someone point me to a list of GSoC ideas this year? 2. Is it okay, if I take up projects related to ideas, that have not yet been implemented. For example, a quick search tells me Improving GMM has not been implemented. Thanks. -- CenturyLink Cloud: The Leader in Enterprise Cloud Services. Learn Why More Businesses Are Choosing CenturyLink Cloud For Critical Workloads, Development Environments Everything In Between. Get a Quote or Start a Free Trial Today. http://pubads.g.doubleclick.net/gampad/clk?id=119420431iu=/4140/ostg.clktrk___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
