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Datasets/ml-100k/README
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Datasets/ml-100k/README
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SUMMARY & USAGE LICENSE
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=============================================
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MovieLens data sets were collected by the GroupLens Research Project
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at the University of Minnesota.
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This data set consists of:
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* 100,000 ratings (1-5) from 943 users on 1682 movies.
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* Each user has rated at least 20 movies.
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* Simple demographic info for the users (age, gender, occupation, zip)
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The data was collected through the MovieLens web site
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(movielens.umn.edu) during the seven-month period from September 19th,
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1997 through April 22nd, 1998. This data has been cleaned up - users
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who had less than 20 ratings or did not have complete demographic
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information were removed from this data set. Detailed descriptions of
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the data file can be found at the end of this file.
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Neither the University of Minnesota nor any of the researchers
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involved can guarantee the correctness of the data, its suitability
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for any particular purpose, or the validity of results based on the
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use of the data set. The data set may be used for any research
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purposes under the following conditions:
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* The user may not state or imply any endorsement from the
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University of Minnesota or the GroupLens Research Group.
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* The user must acknowledge the use of the data set in
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publications resulting from the use of the data set
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(see below for citation information).
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* The user may not redistribute the data without separate
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|
permission.
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* The user may not use this information for any commercial or
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revenue-bearing purposes without first obtaining permission
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|
from a faculty member of the GroupLens Research Project at the
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University of Minnesota.
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If you have any further questions or comments, please contact GroupLens
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<grouplens-info@cs.umn.edu>.
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CITATION
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==============================================
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To acknowledge use of the dataset in publications, please cite the
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following paper:
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F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets:
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History and Context. ACM Transactions on Interactive Intelligent
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Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages.
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DOI=http://dx.doi.org/10.1145/2827872
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ACKNOWLEDGEMENTS
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==============================================
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Thanks to Al Borchers for cleaning up this data and writing the
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accompanying scripts.
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PUBLISHED WORK THAT HAS USED THIS DATASET
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==============================================
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Herlocker, J., Konstan, J., Borchers, A., Riedl, J.. An Algorithmic
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Framework for Performing Collaborative Filtering. Proceedings of the
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1999 Conference on Research and Development in Information
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Retrieval. Aug. 1999.
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FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
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==============================================
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The GroupLens Research Project is a research group in the Department
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of Computer Science and Engineering at the University of Minnesota.
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Members of the GroupLens Research Project are involved in many
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research projects related to the fields of information filtering,
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collaborative filtering, and recommender systems. The project is lead
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by professors John Riedl and Joseph Konstan. The project began to
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explore automated collaborative filtering in 1992, but is most well
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known for its world wide trial of an automated collaborative filtering
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system for Usenet news in 1996. The technology developed in the
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Usenet trial formed the base for the formation of Net Perceptions,
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Inc., which was founded by members of GroupLens Research. Since then
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the project has expanded its scope to research overall information
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filtering solutions, integrating in content-based methods as well as
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improving current collaborative filtering technology.
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Further information on the GroupLens Research project, including
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|
research publications, can be found at the following web site:
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http://www.grouplens.org/
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GroupLens Research currently operates a movie recommender based on
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collaborative filtering:
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http://www.movielens.org/
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DETAILED DESCRIPTIONS OF DATA FILES
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==============================================
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Here are brief descriptions of the data.
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ml-data.tar.gz -- Compressed tar file. To rebuild the u data files do this:
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gunzip ml-data.tar.gz
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tar xvf ml-data.tar
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mku.sh
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u.data -- The full u data set, 100000 ratings by 943 users on 1682 items.
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Each user has rated at least 20 movies. Users and items are
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numbered consecutively from 1. The data is randomly
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ordered. This is a tab separated list of
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user id | item id | rating | timestamp.
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The time stamps are unix seconds since 1/1/1970 UTC
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u.info -- The number of users, items, and ratings in the u data set.
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u.item -- Information about the items (movies); this is a tab separated
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list of
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movie id | movie title | release date | video release date |
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IMDb URL | unknown | Action | Adventure | Animation |
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Children's | Comedy | Crime | Documentary | Drama | Fantasy |
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Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi |
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Thriller | War | Western |
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The last 19 fields are the genres, a 1 indicates the movie
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is of that genre, a 0 indicates it is not; movies can be in
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several genres at once.
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The movie ids are the ones used in the u.data data set.
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u.genre -- A list of the genres.
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u.user -- Demographic information about the users; this is a tab
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separated list of
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user id | age | gender | occupation | zip code
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The user ids are the ones used in the u.data data set.
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u.occupation -- A list of the occupations.
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u1.base -- The data sets u1.base and u1.test through u5.base and u5.test
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u1.test are 80%/20% splits of the u data into training and test data.
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u2.base Each of u1, ..., u5 have disjoint test sets; this if for
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u2.test 5 fold cross validation (where you repeat your experiment
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u3.base with each training and test set and average the results).
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u3.test These data sets can be generated from u.data by mku.sh.
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u4.base
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u4.test
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u5.base
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u5.test
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ua.base -- The data sets ua.base, ua.test, ub.base, and ub.test
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ua.test split the u data into a training set and a test set with
|
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|
ub.base exactly 10 ratings per user in the test set. The sets
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ub.test ua.test and ub.test are disjoint. These data sets can
|
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|
be generated from u.data by mku.sh.
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allbut.pl -- The script that generates training and test sets where
|
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|
all but n of a users ratings are in the training data.
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mku.sh -- A shell script to generate all the u data sets from u.data.
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34
Datasets/ml-100k/allbut.pl
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34
Datasets/ml-100k/allbut.pl
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#!/usr/local/bin/perl
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# get args
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if (@ARGV < 3) {
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print STDERR "Usage: $0 base_name start stop max_test [ratings ...]\n";
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exit 1;
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|
}
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$basename = shift;
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$start = shift;
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$stop = shift;
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$maxtest = shift;
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# open files
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open( TESTFILE, ">$basename.test" ) or die "Cannot open $basename.test for writing\n";
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open( BASEFILE, ">$basename.base" ) or die "Cannot open $basename.base for writing\n";
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# init variables
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$testcnt = 0;
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while (<>) {
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($user) = split;
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|
if (! defined $ratingcnt{$user}) {
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$ratingcnt{$user} = 0;
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|
}
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|
++$ratingcnt{$user};
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|
if (($testcnt < $maxtest || $maxtest <= 0)
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|
&& $ratingcnt{$user} >= $start && $ratingcnt{$user} <= $stop) {
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|
++$testcnt;
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|
print TESTFILE;
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|
}
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|
else {
|
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|
print BASEFILE;
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|
}
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|
}
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25
Datasets/ml-100k/mku.sh
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25
Datasets/ml-100k/mku.sh
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|
#!/bin/sh
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trap `rm -f tmp.$$; exit 1` 1 2 15
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for i in 1 2 3 4 5
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|
do
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|
head -`expr $i \* 20000` u.data | tail -20000 > tmp.$$
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|
sort -t" " -k 1,1n -k 2,2n tmp.$$ > u$i.test
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|
head -`expr \( $i - 1 \) \* 20000` u.data > tmp.$$
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|
tail -`expr \( 5 - $i \) \* 20000` u.data >> tmp.$$
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|
sort -t" " -k 1,1n -k 2,2n tmp.$$ > u$i.base
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|
done
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allbut.pl ua 1 10 100000 u.data
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sort -t" " -k 1,1n -k 2,2n ua.base > tmp.$$
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mv tmp.$$ ua.base
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sort -t" " -k 1,1n -k 2,2n ua.test > tmp.$$
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|
mv tmp.$$ ua.test
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allbut.pl ub 11 20 100000 u.data
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sort -t" " -k 1,1n -k 2,2n ub.base > tmp.$$
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|
mv tmp.$$ ub.base
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|
sort -t" " -k 1,1n -k 2,2n ub.test > tmp.$$
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|
mv tmp.$$ ub.test
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1683
Datasets/ml-100k/movies.csv
Normal file
1683
Datasets/ml-100k/movies.csv
Normal file
File diff suppressed because it is too large
Load Diff
20000
Datasets/ml-100k/test.csv
Normal file
20000
Datasets/ml-100k/test.csv
Normal file
File diff suppressed because it is too large
Load Diff
80000
Datasets/ml-100k/train.csv
Normal file
80000
Datasets/ml-100k/train.csv
Normal file
File diff suppressed because it is too large
Load Diff
100000
Datasets/ml-100k/u.data
Normal file
100000
Datasets/ml-100k/u.data
Normal file
File diff suppressed because it is too large
Load Diff
20
Datasets/ml-100k/u.genre
Normal file
20
Datasets/ml-100k/u.genre
Normal file
@ -0,0 +1,20 @@
|
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|
unknown|0
|
||||||
|
Action|1
|
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|
Adventure|2
|
||||||
|
Animation|3
|
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|
Children's|4
|
||||||
|
Comedy|5
|
||||||
|
Crime|6
|
||||||
|
Documentary|7
|
||||||
|
Drama|8
|
||||||
|
Fantasy|9
|
||||||
|
Film-Noir|10
|
||||||
|
Horror|11
|
||||||
|
Musical|12
|
||||||
|
Mystery|13
|
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|
Romance|14
|
||||||
|
Sci-Fi|15
|
||||||
|
Thriller|16
|
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|
War|17
|
||||||
|
Western|18
|
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|
|
||||||
3
Datasets/ml-100k/u.info
Normal file
3
Datasets/ml-100k/u.info
Normal file
@ -0,0 +1,3 @@
|
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|
943 users
|
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|
1682 items
|
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|
100000 ratings
|
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1682
Datasets/ml-100k/u.item
Normal file
1682
Datasets/ml-100k/u.item
Normal file
File diff suppressed because it is too large
Load Diff
21
Datasets/ml-100k/u.occupation
Normal file
21
Datasets/ml-100k/u.occupation
Normal file
@ -0,0 +1,21 @@
|
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|
administrator
|
||||||
|
artist
|
||||||
|
doctor
|
||||||
|
educator
|
||||||
|
engineer
|
||||||
|
entertainment
|
||||||
|
executive
|
||||||
|
healthcare
|
||||||
|
homemaker
|
||||||
|
lawyer
|
||||||
|
librarian
|
||||||
|
marketing
|
||||||
|
none
|
||||||
|
other
|
||||||
|
programmer
|
||||||
|
retired
|
||||||
|
salesman
|
||||||
|
scientist
|
||||||
|
student
|
||||||
|
technician
|
||||||
|
writer
|
||||||
943
Datasets/ml-100k/u.user
Normal file
943
Datasets/ml-100k/u.user
Normal file
@ -0,0 +1,943 @@
|
|||||||
|
1|24|M|technician|85711
|
||||||
|
2|53|F|other|94043
|
||||||
|
3|23|M|writer|32067
|
||||||
|
4|24|M|technician|43537
|
||||||
|
5|33|F|other|15213
|
||||||
|
6|42|M|executive|98101
|
||||||
|
7|57|M|administrator|91344
|
||||||
|
8|36|M|administrator|05201
|
||||||
|
9|29|M|student|01002
|
||||||
|
10|53|M|lawyer|90703
|
||||||
|
11|39|F|other|30329
|
||||||
|
12|28|F|other|06405
|
||||||
|
13|47|M|educator|29206
|
||||||
|
14|45|M|scientist|55106
|
||||||
|
15|49|F|educator|97301
|
||||||
|
16|21|M|entertainment|10309
|
||||||
|
17|30|M|programmer|06355
|
||||||
|
18|35|F|other|37212
|
||||||
|
19|40|M|librarian|02138
|
||||||
|
20|42|F|homemaker|95660
|
||||||
|
21|26|M|writer|30068
|
||||||
|
22|25|M|writer|40206
|
||||||
|
23|30|F|artist|48197
|
||||||
|
24|21|F|artist|94533
|
||||||
|
25|39|M|engineer|55107
|
||||||
|
26|49|M|engineer|21044
|
||||||
|
27|40|F|librarian|30030
|
||||||
|
28|32|M|writer|55369
|
||||||
|
29|41|M|programmer|94043
|
||||||
|
30|7|M|student|55436
|
||||||
|
31|24|M|artist|10003
|
||||||
|
32|28|F|student|78741
|
||||||
|
33|23|M|student|27510
|
||||||
|
34|38|F|administrator|42141
|
||||||
|
35|20|F|homemaker|42459
|
||||||
|
36|19|F|student|93117
|
||||||
|
37|23|M|student|55105
|
||||||
|
38|28|F|other|54467
|
||||||
|
39|41|M|entertainment|01040
|
||||||
|
40|38|M|scientist|27514
|
||||||
|
41|33|M|engineer|80525
|
||||||
|
42|30|M|administrator|17870
|
||||||
|
43|29|F|librarian|20854
|
||||||
|
44|26|M|technician|46260
|
||||||
|
45|29|M|programmer|50233
|
||||||
|
46|27|F|marketing|46538
|
||||||
|
47|53|M|marketing|07102
|
||||||
|
48|45|M|administrator|12550
|
||||||
|
49|23|F|student|76111
|
||||||
|
50|21|M|writer|52245
|
||||||
|
51|28|M|educator|16509
|
||||||
|
52|18|F|student|55105
|
||||||
|
53|26|M|programmer|55414
|
||||||
|
54|22|M|executive|66315
|
||||||
|
55|37|M|programmer|01331
|
||||||
|
56|25|M|librarian|46260
|
||||||
|
57|16|M|none|84010
|
||||||
|
58|27|M|programmer|52246
|
||||||
|
59|49|M|educator|08403
|
||||||
|
60|50|M|healthcare|06472
|
||||||
|
61|36|M|engineer|30040
|
||||||
|
62|27|F|administrator|97214
|
||||||
|
63|31|M|marketing|75240
|
||||||
|
64|32|M|educator|43202
|
||||||
|
65|51|F|educator|48118
|
||||||
|
66|23|M|student|80521
|
||||||
|
67|17|M|student|60402
|
||||||
|
68|19|M|student|22904
|
||||||
|
69|24|M|engineer|55337
|
||||||
|
70|27|M|engineer|60067
|
||||||
|
71|39|M|scientist|98034
|
||||||
|
72|48|F|administrator|73034
|
||||||
|
73|24|M|student|41850
|
||||||
|
74|39|M|scientist|T8H1N
|
||||||
|
75|24|M|entertainment|08816
|
||||||
|
76|20|M|student|02215
|
||||||
|
77|30|M|technician|29379
|
||||||
|
78|26|M|administrator|61801
|
||||||
|
79|39|F|administrator|03755
|
||||||
|
80|34|F|administrator|52241
|
||||||
|
81|21|M|student|21218
|
||||||
|
82|50|M|programmer|22902
|
||||||
|
83|40|M|other|44133
|
||||||
|
84|32|M|executive|55369
|
||||||
|
85|51|M|educator|20003
|
||||||
|
86|26|M|administrator|46005
|
||||||
|
87|47|M|administrator|89503
|
||||||
|
88|49|F|librarian|11701
|
||||||
|
89|43|F|administrator|68106
|
||||||
|
90|60|M|educator|78155
|
||||||
|
91|55|M|marketing|01913
|
||||||
|
92|32|M|entertainment|80525
|
||||||
|
93|48|M|executive|23112
|
||||||
|
94|26|M|student|71457
|
||||||
|
95|31|M|administrator|10707
|
||||||
|
96|25|F|artist|75206
|
||||||
|
97|43|M|artist|98006
|
||||||
|
98|49|F|executive|90291
|
||||||
|
99|20|M|student|63129
|
||||||
|
100|36|M|executive|90254
|
||||||
|
101|15|M|student|05146
|
||||||
|
102|38|M|programmer|30220
|
||||||
|
103|26|M|student|55108
|
||||||
|
104|27|M|student|55108
|
||||||
|
105|24|M|engineer|94043
|
||||||
|
106|61|M|retired|55125
|
||||||
|
107|39|M|scientist|60466
|
||||||
|
108|44|M|educator|63130
|
||||||
|
109|29|M|other|55423
|
||||||
|
110|19|M|student|77840
|
||||||
|
111|57|M|engineer|90630
|
||||||
|
112|30|M|salesman|60613
|
||||||
|
113|47|M|executive|95032
|
||||||
|
114|27|M|programmer|75013
|
||||||
|
115|31|M|engineer|17110
|
||||||
|
116|40|M|healthcare|97232
|
||||||
|
117|20|M|student|16125
|
||||||
|
118|21|M|administrator|90210
|
||||||
|
119|32|M|programmer|67401
|
||||||
|
120|47|F|other|06260
|
||||||
|
121|54|M|librarian|99603
|
||||||
|
122|32|F|writer|22206
|
||||||
|
123|48|F|artist|20008
|
||||||
|
124|34|M|student|60615
|
||||||
|
125|30|M|lawyer|22202
|
||||||
|
126|28|F|lawyer|20015
|
||||||
|
127|33|M|none|73439
|
||||||
|
128|24|F|marketing|20009
|
||||||
|
129|36|F|marketing|07039
|
||||||
|
130|20|M|none|60115
|
||||||
|
131|59|F|administrator|15237
|
||||||
|
132|24|M|other|94612
|
||||||
|
133|53|M|engineer|78602
|
||||||
|
134|31|M|programmer|80236
|
||||||
|
135|23|M|student|38401
|
||||||
|
136|51|M|other|97365
|
||||||
|
137|50|M|educator|84408
|
||||||
|
138|46|M|doctor|53211
|
||||||
|
139|20|M|student|08904
|
||||||
|
140|30|F|student|32250
|
||||||
|
141|49|M|programmer|36117
|
||||||
|
142|13|M|other|48118
|
||||||
|
143|42|M|technician|08832
|
||||||
|
144|53|M|programmer|20910
|
||||||
|
145|31|M|entertainment|V3N4P
|
||||||
|
146|45|M|artist|83814
|
||||||
|
147|40|F|librarian|02143
|
||||||
|
148|33|M|engineer|97006
|
||||||
|
149|35|F|marketing|17325
|
||||||
|
150|20|F|artist|02139
|
||||||
|
151|38|F|administrator|48103
|
||||||
|
152|33|F|educator|68767
|
||||||
|
153|25|M|student|60641
|
||||||
|
154|25|M|student|53703
|
||||||
|
155|32|F|other|11217
|
||||||
|
156|25|M|educator|08360
|
||||||
|
157|57|M|engineer|70808
|
||||||
|
158|50|M|educator|27606
|
||||||
|
159|23|F|student|55346
|
||||||
|
160|27|M|programmer|66215
|
||||||
|
161|50|M|lawyer|55104
|
||||||
|
162|25|M|artist|15610
|
||||||
|
163|49|M|administrator|97212
|
||||||
|
164|47|M|healthcare|80123
|
||||||
|
165|20|F|other|53715
|
||||||
|
166|47|M|educator|55113
|
||||||
|
167|37|M|other|L9G2B
|
||||||
|
168|48|M|other|80127
|
||||||
|
169|52|F|other|53705
|
||||||
|
170|53|F|healthcare|30067
|
||||||
|
171|48|F|educator|78750
|
||||||
|
172|55|M|marketing|22207
|
||||||
|
173|56|M|other|22306
|
||||||
|
174|30|F|administrator|52302
|
||||||
|
175|26|F|scientist|21911
|
||||||
|
176|28|M|scientist|07030
|
||||||
|
177|20|M|programmer|19104
|
||||||
|
178|26|M|other|49512
|
||||||
|
179|15|M|entertainment|20755
|
||||||
|
180|22|F|administrator|60202
|
||||||
|
181|26|M|executive|21218
|
||||||
|
182|36|M|programmer|33884
|
||||||
|
183|33|M|scientist|27708
|
||||||
|
184|37|M|librarian|76013
|
||||||
|
185|53|F|librarian|97403
|
||||||
|
186|39|F|executive|00000
|
||||||
|
187|26|M|educator|16801
|
||||||
|
188|42|M|student|29440
|
||||||
|
189|32|M|artist|95014
|
||||||
|
190|30|M|administrator|95938
|
||||||
|
191|33|M|administrator|95161
|
||||||
|
192|42|M|educator|90840
|
||||||
|
193|29|M|student|49931
|
||||||
|
194|38|M|administrator|02154
|
||||||
|
195|42|M|scientist|93555
|
||||||
|
196|49|M|writer|55105
|
||||||
|
197|55|M|technician|75094
|
||||||
|
198|21|F|student|55414
|
||||||
|
199|30|M|writer|17604
|
||||||
|
200|40|M|programmer|93402
|
||||||
|
201|27|M|writer|E2A4H
|
||||||
|
202|41|F|educator|60201
|
||||||
|
203|25|F|student|32301
|
||||||
|
204|52|F|librarian|10960
|
||||||
|
205|47|M|lawyer|06371
|
||||||
|
206|14|F|student|53115
|
||||||
|
207|39|M|marketing|92037
|
||||||
|
208|43|M|engineer|01720
|
||||||
|
209|33|F|educator|85710
|
||||||
|
210|39|M|engineer|03060
|
||||||
|
211|66|M|salesman|32605
|
||||||
|
212|49|F|educator|61401
|
||||||
|
213|33|M|executive|55345
|
||||||
|
214|26|F|librarian|11231
|
||||||
|
215|35|M|programmer|63033
|
||||||
|
216|22|M|engineer|02215
|
||||||
|
217|22|M|other|11727
|
||||||
|
218|37|M|administrator|06513
|
||||||
|
219|32|M|programmer|43212
|
||||||
|
220|30|M|librarian|78205
|
||||||
|
221|19|M|student|20685
|
||||||
|
222|29|M|programmer|27502
|
||||||
|
223|19|F|student|47906
|
||||||
|
224|31|F|educator|43512
|
||||||
|
225|51|F|administrator|58202
|
||||||
|
226|28|M|student|92103
|
||||||
|
227|46|M|executive|60659
|
||||||
|
228|21|F|student|22003
|
||||||
|
229|29|F|librarian|22903
|
||||||
|
230|28|F|student|14476
|
||||||
|
231|48|M|librarian|01080
|
||||||
|
232|45|M|scientist|99709
|
||||||
|
233|38|M|engineer|98682
|
||||||
|
234|60|M|retired|94702
|
||||||
|
235|37|M|educator|22973
|
||||||
|
236|44|F|writer|53214
|
||||||
|
237|49|M|administrator|63146
|
||||||
|
238|42|F|administrator|44124
|
||||||
|
239|39|M|artist|95628
|
||||||
|
240|23|F|educator|20784
|
||||||
|
241|26|F|student|20001
|
||||||
|
242|33|M|educator|31404
|
||||||
|
243|33|M|educator|60201
|
||||||
|
244|28|M|technician|80525
|
||||||
|
245|22|M|student|55109
|
||||||
|
246|19|M|student|28734
|
||||||
|
247|28|M|engineer|20770
|
||||||
|
248|25|M|student|37235
|
||||||
|
249|25|M|student|84103
|
||||||
|
250|29|M|executive|95110
|
||||||
|
251|28|M|doctor|85032
|
||||||
|
252|42|M|engineer|07733
|
||||||
|
253|26|F|librarian|22903
|
||||||
|
254|44|M|educator|42647
|
||||||
|
255|23|M|entertainment|07029
|
||||||
|
256|35|F|none|39042
|
||||||
|
257|17|M|student|77005
|
||||||
|
258|19|F|student|77801
|
||||||
|
259|21|M|student|48823
|
||||||
|
260|40|F|artist|89801
|
||||||
|
261|28|M|administrator|85202
|
||||||
|
262|19|F|student|78264
|
||||||
|
263|41|M|programmer|55346
|
||||||
|
264|36|F|writer|90064
|
||||||
|
265|26|M|executive|84601
|
||||||
|
266|62|F|administrator|78756
|
||||||
|
267|23|M|engineer|83716
|
||||||
|
268|24|M|engineer|19422
|
||||||
|
269|31|F|librarian|43201
|
||||||
|
270|18|F|student|63119
|
||||||
|
271|51|M|engineer|22932
|
||||||
|
272|33|M|scientist|53706
|
||||||
|
273|50|F|other|10016
|
||||||
|
274|20|F|student|55414
|
||||||
|
275|38|M|engineer|92064
|
||||||
|
276|21|M|student|95064
|
||||||
|
277|35|F|administrator|55406
|
||||||
|
278|37|F|librarian|30033
|
||||||
|
279|33|M|programmer|85251
|
||||||
|
280|30|F|librarian|22903
|
||||||
|
281|15|F|student|06059
|
||||||
|
282|22|M|administrator|20057
|
||||||
|
283|28|M|programmer|55305
|
||||||
|
284|40|M|executive|92629
|
||||||
|
285|25|M|programmer|53713
|
||||||
|
286|27|M|student|15217
|
||||||
|
287|21|M|salesman|31211
|
||||||
|
288|34|M|marketing|23226
|
||||||
|
289|11|M|none|94619
|
||||||
|
290|40|M|engineer|93550
|
||||||
|
291|19|M|student|44106
|
||||||
|
292|35|F|programmer|94703
|
||||||
|
293|24|M|writer|60804
|
||||||
|
294|34|M|technician|92110
|
||||||
|
295|31|M|educator|50325
|
||||||
|
296|43|F|administrator|16803
|
||||||
|
297|29|F|educator|98103
|
||||||
|
298|44|M|executive|01581
|
||||||
|
299|29|M|doctor|63108
|
||||||
|
300|26|F|programmer|55106
|
||||||
|
301|24|M|student|55439
|
||||||
|
302|42|M|educator|77904
|
||||||
|
303|19|M|student|14853
|
||||||
|
304|22|F|student|71701
|
||||||
|
305|23|M|programmer|94086
|
||||||
|
306|45|M|other|73132
|
||||||
|
307|25|M|student|55454
|
||||||
|
308|60|M|retired|95076
|
||||||
|
309|40|M|scientist|70802
|
||||||
|
310|37|M|educator|91711
|
||||||
|
311|32|M|technician|73071
|
||||||
|
312|48|M|other|02110
|
||||||
|
313|41|M|marketing|60035
|
||||||
|
314|20|F|student|08043
|
||||||
|
315|31|M|educator|18301
|
||||||
|
316|43|F|other|77009
|
||||||
|
317|22|M|administrator|13210
|
||||||
|
318|65|M|retired|06518
|
||||||
|
319|38|M|programmer|22030
|
||||||
|
320|19|M|student|24060
|
||||||
|
321|49|F|educator|55413
|
||||||
|
322|20|M|student|50613
|
||||||
|
323|21|M|student|19149
|
||||||
|
324|21|F|student|02176
|
||||||
|
325|48|M|technician|02139
|
||||||
|
326|41|M|administrator|15235
|
||||||
|
327|22|M|student|11101
|
||||||
|
328|51|M|administrator|06779
|
||||||
|
329|48|M|educator|01720
|
||||||
|
330|35|F|educator|33884
|
||||||
|
331|33|M|entertainment|91344
|
||||||
|
332|20|M|student|40504
|
||||||
|
333|47|M|other|V0R2M
|
||||||
|
334|32|M|librarian|30002
|
||||||
|
335|45|M|executive|33775
|
||||||
|
336|23|M|salesman|42101
|
||||||
|
337|37|M|scientist|10522
|
||||||
|
338|39|F|librarian|59717
|
||||||
|
339|35|M|lawyer|37901
|
||||||
|
340|46|M|engineer|80123
|
||||||
|
341|17|F|student|44405
|
||||||
|
342|25|F|other|98006
|
||||||
|
343|43|M|engineer|30093
|
||||||
|
344|30|F|librarian|94117
|
||||||
|
345|28|F|librarian|94143
|
||||||
|
346|34|M|other|76059
|
||||||
|
347|18|M|student|90210
|
||||||
|
348|24|F|student|45660
|
||||||
|
349|68|M|retired|61455
|
||||||
|
350|32|M|student|97301
|
||||||
|
351|61|M|educator|49938
|
||||||
|
352|37|F|programmer|55105
|
||||||
|
353|25|M|scientist|28480
|
||||||
|
354|29|F|librarian|48197
|
||||||
|
355|25|M|student|60135
|
||||||
|
356|32|F|homemaker|92688
|
||||||
|
357|26|M|executive|98133
|
||||||
|
358|40|M|educator|10022
|
||||||
|
359|22|M|student|61801
|
||||||
|
360|51|M|other|98027
|
||||||
|
361|22|M|student|44074
|
||||||
|
362|35|F|homemaker|85233
|
||||||
|
363|20|M|student|87501
|
||||||
|
364|63|M|engineer|01810
|
||||||
|
365|29|M|lawyer|20009
|
||||||
|
366|20|F|student|50670
|
||||||
|
367|17|M|student|37411
|
||||||
|
368|18|M|student|92113
|
||||||
|
369|24|M|student|91335
|
||||||
|
370|52|M|writer|08534
|
||||||
|
371|36|M|engineer|99206
|
||||||
|
372|25|F|student|66046
|
||||||
|
373|24|F|other|55116
|
||||||
|
374|36|M|executive|78746
|
||||||
|
375|17|M|entertainment|37777
|
||||||
|
376|28|F|other|10010
|
||||||
|
377|22|M|student|18015
|
||||||
|
378|35|M|student|02859
|
||||||
|
379|44|M|programmer|98117
|
||||||
|
380|32|M|engineer|55117
|
||||||
|
381|33|M|artist|94608
|
||||||
|
382|45|M|engineer|01824
|
||||||
|
383|42|M|administrator|75204
|
||||||
|
384|52|M|programmer|45218
|
||||||
|
385|36|M|writer|10003
|
||||||
|
386|36|M|salesman|43221
|
||||||
|
387|33|M|entertainment|37412
|
||||||
|
388|31|M|other|36106
|
||||||
|
389|44|F|writer|83702
|
||||||
|
390|42|F|writer|85016
|
||||||
|
391|23|M|student|84604
|
||||||
|
392|52|M|writer|59801
|
||||||
|
393|19|M|student|83686
|
||||||
|
394|25|M|administrator|96819
|
||||||
|
395|43|M|other|44092
|
||||||
|
396|57|M|engineer|94551
|
||||||
|
397|17|M|student|27514
|
||||||
|
398|40|M|other|60008
|
||||||
|
399|25|M|other|92374
|
||||||
|
400|33|F|administrator|78213
|
||||||
|
401|46|F|healthcare|84107
|
||||||
|
402|30|M|engineer|95129
|
||||||
|
403|37|M|other|06811
|
||||||
|
404|29|F|programmer|55108
|
||||||
|
405|22|F|healthcare|10019
|
||||||
|
406|52|M|educator|93109
|
||||||
|
407|29|M|engineer|03261
|
||||||
|
408|23|M|student|61755
|
||||||
|
409|48|M|administrator|98225
|
||||||
|
410|30|F|artist|94025
|
||||||
|
411|34|M|educator|44691
|
||||||
|
412|25|M|educator|15222
|
||||||
|
413|55|M|educator|78212
|
||||||
|
414|24|M|programmer|38115
|
||||||
|
415|39|M|educator|85711
|
||||||
|
416|20|F|student|92626
|
||||||
|
417|27|F|other|48103
|
||||||
|
418|55|F|none|21206
|
||||||
|
419|37|M|lawyer|43215
|
||||||
|
420|53|M|educator|02140
|
||||||
|
421|38|F|programmer|55105
|
||||||
|
422|26|M|entertainment|94533
|
||||||
|
423|64|M|other|91606
|
||||||
|
424|36|F|marketing|55422
|
||||||
|
425|19|M|student|58644
|
||||||
|
426|55|M|educator|01602
|
||||||
|
427|51|M|doctor|85258
|
||||||
|
428|28|M|student|55414
|
||||||
|
429|27|M|student|29205
|
||||||
|
430|38|M|scientist|98199
|
||||||
|
431|24|M|marketing|92629
|
||||||
|
432|22|M|entertainment|50311
|
||||||
|
433|27|M|artist|11211
|
||||||
|
434|16|F|student|49705
|
||||||
|
435|24|M|engineer|60007
|
||||||
|
436|30|F|administrator|17345
|
||||||
|
437|27|F|other|20009
|
||||||
|
438|51|F|administrator|43204
|
||||||
|
439|23|F|administrator|20817
|
||||||
|
440|30|M|other|48076
|
||||||
|
441|50|M|technician|55013
|
||||||
|
442|22|M|student|85282
|
||||||
|
443|35|M|salesman|33308
|
||||||
|
444|51|F|lawyer|53202
|
||||||
|
445|21|M|writer|92653
|
||||||
|
446|57|M|educator|60201
|
||||||
|
447|30|M|administrator|55113
|
||||||
|
448|23|M|entertainment|10021
|
||||||
|
449|23|M|librarian|55021
|
||||||
|
450|35|F|educator|11758
|
||||||
|
451|16|M|student|48446
|
||||||
|
452|35|M|administrator|28018
|
||||||
|
453|18|M|student|06333
|
||||||
|
454|57|M|other|97330
|
||||||
|
455|48|M|administrator|83709
|
||||||
|
456|24|M|technician|31820
|
||||||
|
457|33|F|salesman|30011
|
||||||
|
458|47|M|technician|Y1A6B
|
||||||
|
459|22|M|student|29201
|
||||||
|
460|44|F|other|60630
|
||||||
|
461|15|M|student|98102
|
||||||
|
462|19|F|student|02918
|
||||||
|
463|48|F|healthcare|75218
|
||||||
|
464|60|M|writer|94583
|
||||||
|
465|32|M|other|05001
|
||||||
|
466|22|M|student|90804
|
||||||
|
467|29|M|engineer|91201
|
||||||
|
468|28|M|engineer|02341
|
||||||
|
469|60|M|educator|78628
|
||||||
|
470|24|M|programmer|10021
|
||||||
|
471|10|M|student|77459
|
||||||
|
472|24|M|student|87544
|
||||||
|
473|29|M|student|94708
|
||||||
|
474|51|M|executive|93711
|
||||||
|
475|30|M|programmer|75230
|
||||||
|
476|28|M|student|60440
|
||||||
|
477|23|F|student|02125
|
||||||
|
478|29|M|other|10019
|
||||||
|
479|30|M|educator|55409
|
||||||
|
480|57|M|retired|98257
|
||||||
|
481|73|M|retired|37771
|
||||||
|
482|18|F|student|40256
|
||||||
|
483|29|M|scientist|43212
|
||||||
|
484|27|M|student|21208
|
||||||
|
485|44|F|educator|95821
|
||||||
|
486|39|M|educator|93101
|
||||||
|
487|22|M|engineer|92121
|
||||||
|
488|48|M|technician|21012
|
||||||
|
489|55|M|other|45218
|
||||||
|
490|29|F|artist|V5A2B
|
||||||
|
491|43|F|writer|53711
|
||||||
|
492|57|M|educator|94618
|
||||||
|
493|22|M|engineer|60090
|
||||||
|
494|38|F|administrator|49428
|
||||||
|
495|29|M|engineer|03052
|
||||||
|
496|21|F|student|55414
|
||||||
|
497|20|M|student|50112
|
||||||
|
498|26|M|writer|55408
|
||||||
|
499|42|M|programmer|75006
|
||||||
|
500|28|M|administrator|94305
|
||||||
|
501|22|M|student|10025
|
||||||
|
502|22|M|student|23092
|
||||||
|
503|50|F|writer|27514
|
||||||
|
504|40|F|writer|92115
|
||||||
|
505|27|F|other|20657
|
||||||
|
506|46|M|programmer|03869
|
||||||
|
507|18|F|writer|28450
|
||||||
|
508|27|M|marketing|19382
|
||||||
|
509|23|M|administrator|10011
|
||||||
|
510|34|M|other|98038
|
||||||
|
511|22|M|student|21250
|
||||||
|
512|29|M|other|20090
|
||||||
|
513|43|M|administrator|26241
|
||||||
|
514|27|M|programmer|20707
|
||||||
|
515|53|M|marketing|49508
|
||||||
|
516|53|F|librarian|10021
|
||||||
|
517|24|M|student|55454
|
||||||
|
518|49|F|writer|99709
|
||||||
|
519|22|M|other|55320
|
||||||
|
520|62|M|healthcare|12603
|
||||||
|
521|19|M|student|02146
|
||||||
|
522|36|M|engineer|55443
|
||||||
|
523|50|F|administrator|04102
|
||||||
|
524|56|M|educator|02159
|
||||||
|
525|27|F|administrator|19711
|
||||||
|
526|30|M|marketing|97124
|
||||||
|
527|33|M|librarian|12180
|
||||||
|
528|18|M|student|55104
|
||||||
|
529|47|F|administrator|44224
|
||||||
|
530|29|M|engineer|94040
|
||||||
|
531|30|F|salesman|97408
|
||||||
|
532|20|M|student|92705
|
||||||
|
533|43|M|librarian|02324
|
||||||
|
534|20|M|student|05464
|
||||||
|
535|45|F|educator|80302
|
||||||
|
536|38|M|engineer|30078
|
||||||
|
537|36|M|engineer|22902
|
||||||
|
538|31|M|scientist|21010
|
||||||
|
539|53|F|administrator|80303
|
||||||
|
540|28|M|engineer|91201
|
||||||
|
541|19|F|student|84302
|
||||||
|
542|21|M|student|60515
|
||||||
|
543|33|M|scientist|95123
|
||||||
|
544|44|F|other|29464
|
||||||
|
545|27|M|technician|08052
|
||||||
|
546|36|M|executive|22911
|
||||||
|
547|50|M|educator|14534
|
||||||
|
548|51|M|writer|95468
|
||||||
|
549|42|M|scientist|45680
|
||||||
|
550|16|F|student|95453
|
||||||
|
551|25|M|programmer|55414
|
||||||
|
552|45|M|other|68147
|
||||||
|
553|58|M|educator|62901
|
||||||
|
554|32|M|scientist|62901
|
||||||
|
555|29|F|educator|23227
|
||||||
|
556|35|F|educator|30606
|
||||||
|
557|30|F|writer|11217
|
||||||
|
558|56|F|writer|63132
|
||||||
|
559|69|M|executive|10022
|
||||||
|
560|32|M|student|10003
|
||||||
|
561|23|M|engineer|60005
|
||||||
|
562|54|F|administrator|20879
|
||||||
|
563|39|F|librarian|32707
|
||||||
|
564|65|M|retired|94591
|
||||||
|
565|40|M|student|55422
|
||||||
|
566|20|M|student|14627
|
||||||
|
567|24|M|entertainment|10003
|
||||||
|
568|39|M|educator|01915
|
||||||
|
569|34|M|educator|91903
|
||||||
|
570|26|M|educator|14627
|
||||||
|
571|34|M|artist|01945
|
||||||
|
572|51|M|educator|20003
|
||||||
|
573|68|M|retired|48911
|
||||||
|
574|56|M|educator|53188
|
||||||
|
575|33|M|marketing|46032
|
||||||
|
576|48|M|executive|98281
|
||||||
|
577|36|F|student|77845
|
||||||
|
578|31|M|administrator|M7A1A
|
||||||
|
579|32|M|educator|48103
|
||||||
|
580|16|M|student|17961
|
||||||
|
581|37|M|other|94131
|
||||||
|
582|17|M|student|93003
|
||||||
|
583|44|M|engineer|29631
|
||||||
|
584|25|M|student|27511
|
||||||
|
585|69|M|librarian|98501
|
||||||
|
586|20|M|student|79508
|
||||||
|
587|26|M|other|14216
|
||||||
|
588|18|F|student|93063
|
||||||
|
589|21|M|lawyer|90034
|
||||||
|
590|50|M|educator|82435
|
||||||
|
591|57|F|librarian|92093
|
||||||
|
592|18|M|student|97520
|
||||||
|
593|31|F|educator|68767
|
||||||
|
594|46|M|educator|M4J2K
|
||||||
|
595|25|M|programmer|31909
|
||||||
|
596|20|M|artist|77073
|
||||||
|
597|23|M|other|84116
|
||||||
|
598|40|F|marketing|43085
|
||||||
|
599|22|F|student|R3T5K
|
||||||
|
600|34|M|programmer|02320
|
||||||
|
601|19|F|artist|99687
|
||||||
|
602|47|F|other|34656
|
||||||
|
603|21|M|programmer|47905
|
||||||
|
604|39|M|educator|11787
|
||||||
|
605|33|M|engineer|33716
|
||||||
|
606|28|M|programmer|63044
|
||||||
|
607|49|F|healthcare|02154
|
||||||
|
608|22|M|other|10003
|
||||||
|
609|13|F|student|55106
|
||||||
|
610|22|M|student|21227
|
||||||
|
611|46|M|librarian|77008
|
||||||
|
612|36|M|educator|79070
|
||||||
|
613|37|F|marketing|29678
|
||||||
|
614|54|M|educator|80227
|
||||||
|
615|38|M|educator|27705
|
||||||
|
616|55|M|scientist|50613
|
||||||
|
617|27|F|writer|11201
|
||||||
|
618|15|F|student|44212
|
||||||
|
619|17|M|student|44134
|
||||||
|
620|18|F|writer|81648
|
||||||
|
621|17|M|student|60402
|
||||||
|
622|25|M|programmer|14850
|
||||||
|
623|50|F|educator|60187
|
||||||
|
624|19|M|student|30067
|
||||||
|
625|27|M|programmer|20723
|
||||||
|
626|23|M|scientist|19807
|
||||||
|
627|24|M|engineer|08034
|
||||||
|
628|13|M|none|94306
|
||||||
|
629|46|F|other|44224
|
||||||
|
630|26|F|healthcare|55408
|
||||||
|
631|18|F|student|38866
|
||||||
|
632|18|M|student|55454
|
||||||
|
633|35|M|programmer|55414
|
||||||
|
634|39|M|engineer|T8H1N
|
||||||
|
635|22|M|other|23237
|
||||||
|
636|47|M|educator|48043
|
||||||
|
637|30|M|other|74101
|
||||||
|
638|45|M|engineer|01940
|
||||||
|
639|42|F|librarian|12065
|
||||||
|
640|20|M|student|61801
|
||||||
|
641|24|M|student|60626
|
||||||
|
642|18|F|student|95521
|
||||||
|
643|39|M|scientist|55122
|
||||||
|
644|51|M|retired|63645
|
||||||
|
645|27|M|programmer|53211
|
||||||
|
646|17|F|student|51250
|
||||||
|
647|40|M|educator|45810
|
||||||
|
648|43|M|engineer|91351
|
||||||
|
649|20|M|student|39762
|
||||||
|
650|42|M|engineer|83814
|
||||||
|
651|65|M|retired|02903
|
||||||
|
652|35|M|other|22911
|
||||||
|
653|31|M|executive|55105
|
||||||
|
654|27|F|student|78739
|
||||||
|
655|50|F|healthcare|60657
|
||||||
|
656|48|M|educator|10314
|
||||||
|
657|26|F|none|78704
|
||||||
|
658|33|M|programmer|92626
|
||||||
|
659|31|M|educator|54248
|
||||||
|
660|26|M|student|77380
|
||||||
|
661|28|M|programmer|98121
|
||||||
|
662|55|M|librarian|19102
|
||||||
|
663|26|M|other|19341
|
||||||
|
664|30|M|engineer|94115
|
||||||
|
665|25|M|administrator|55412
|
||||||
|
666|44|M|administrator|61820
|
||||||
|
667|35|M|librarian|01970
|
||||||
|
668|29|F|writer|10016
|
||||||
|
669|37|M|other|20009
|
||||||
|
670|30|M|technician|21114
|
||||||
|
671|21|M|programmer|91919
|
||||||
|
672|54|F|administrator|90095
|
||||||
|
673|51|M|educator|22906
|
||||||
|
674|13|F|student|55337
|
||||||
|
675|34|M|other|28814
|
||||||
|
676|30|M|programmer|32712
|
||||||
|
677|20|M|other|99835
|
||||||
|
678|50|M|educator|61462
|
||||||
|
679|20|F|student|54302
|
||||||
|
680|33|M|lawyer|90405
|
||||||
|
681|44|F|marketing|97208
|
||||||
|
682|23|M|programmer|55128
|
||||||
|
683|42|M|librarian|23509
|
||||||
|
684|28|M|student|55414
|
||||||
|
685|32|F|librarian|55409
|
||||||
|
686|32|M|educator|26506
|
||||||
|
687|31|F|healthcare|27713
|
||||||
|
688|37|F|administrator|60476
|
||||||
|
689|25|M|other|45439
|
||||||
|
690|35|M|salesman|63304
|
||||||
|
691|34|M|educator|60089
|
||||||
|
692|34|M|engineer|18053
|
||||||
|
693|43|F|healthcare|85210
|
||||||
|
694|60|M|programmer|06365
|
||||||
|
695|26|M|writer|38115
|
||||||
|
696|55|M|other|94920
|
||||||
|
697|25|M|other|77042
|
||||||
|
698|28|F|programmer|06906
|
||||||
|
699|44|M|other|96754
|
||||||
|
700|17|M|student|76309
|
||||||
|
701|51|F|librarian|56321
|
||||||
|
702|37|M|other|89104
|
||||||
|
703|26|M|educator|49512
|
||||||
|
704|51|F|librarian|91105
|
||||||
|
705|21|F|student|54494
|
||||||
|
706|23|M|student|55454
|
||||||
|
707|56|F|librarian|19146
|
||||||
|
708|26|F|homemaker|96349
|
||||||
|
709|21|M|other|N4T1A
|
||||||
|
710|19|M|student|92020
|
||||||
|
711|22|F|student|15203
|
||||||
|
712|22|F|student|54901
|
||||||
|
713|42|F|other|07204
|
||||||
|
714|26|M|engineer|55343
|
||||||
|
715|21|M|technician|91206
|
||||||
|
716|36|F|administrator|44265
|
||||||
|
717|24|M|technician|84105
|
||||||
|
718|42|M|technician|64118
|
||||||
|
719|37|F|other|V0R2H
|
||||||
|
720|49|F|administrator|16506
|
||||||
|
721|24|F|entertainment|11238
|
||||||
|
722|50|F|homemaker|17331
|
||||||
|
723|26|M|executive|94403
|
||||||
|
724|31|M|executive|40243
|
||||||
|
725|21|M|student|91711
|
||||||
|
726|25|F|administrator|80538
|
||||||
|
727|25|M|student|78741
|
||||||
|
728|58|M|executive|94306
|
||||||
|
729|19|M|student|56567
|
||||||
|
730|31|F|scientist|32114
|
||||||
|
731|41|F|educator|70403
|
||||||
|
732|28|F|other|98405
|
||||||
|
733|44|F|other|60630
|
||||||
|
734|25|F|other|63108
|
||||||
|
735|29|F|healthcare|85719
|
||||||
|
736|48|F|writer|94618
|
||||||
|
737|30|M|programmer|98072
|
||||||
|
738|35|M|technician|95403
|
||||||
|
739|35|M|technician|73162
|
||||||
|
740|25|F|educator|22206
|
||||||
|
741|25|M|writer|63108
|
||||||
|
742|35|M|student|29210
|
||||||
|
743|31|M|programmer|92660
|
||||||
|
744|35|M|marketing|47024
|
||||||
|
745|42|M|writer|55113
|
||||||
|
746|25|M|engineer|19047
|
||||||
|
747|19|M|other|93612
|
||||||
|
748|28|M|administrator|94720
|
||||||
|
749|33|M|other|80919
|
||||||
|
750|28|M|administrator|32303
|
||||||
|
751|24|F|other|90034
|
||||||
|
752|60|M|retired|21201
|
||||||
|
753|56|M|salesman|91206
|
||||||
|
754|59|F|librarian|62901
|
||||||
|
755|44|F|educator|97007
|
||||||
|
756|30|F|none|90247
|
||||||
|
757|26|M|student|55104
|
||||||
|
758|27|M|student|53706
|
||||||
|
759|20|F|student|68503
|
||||||
|
760|35|F|other|14211
|
||||||
|
761|17|M|student|97302
|
||||||
|
762|32|M|administrator|95050
|
||||||
|
763|27|M|scientist|02113
|
||||||
|
764|27|F|educator|62903
|
||||||
|
765|31|M|student|33066
|
||||||
|
766|42|M|other|10960
|
||||||
|
767|70|M|engineer|00000
|
||||||
|
768|29|M|administrator|12866
|
||||||
|
769|39|M|executive|06927
|
||||||
|
770|28|M|student|14216
|
||||||
|
771|26|M|student|15232
|
||||||
|
772|50|M|writer|27105
|
||||||
|
773|20|M|student|55414
|
||||||
|
774|30|M|student|80027
|
||||||
|
775|46|M|executive|90036
|
||||||
|
776|30|M|librarian|51157
|
||||||
|
777|63|M|programmer|01810
|
||||||
|
778|34|M|student|01960
|
||||||
|
779|31|M|student|K7L5J
|
||||||
|
780|49|M|programmer|94560
|
||||||
|
781|20|M|student|48825
|
||||||
|
782|21|F|artist|33205
|
||||||
|
783|30|M|marketing|77081
|
||||||
|
784|47|M|administrator|91040
|
||||||
|
785|32|M|engineer|23322
|
||||||
|
786|36|F|engineer|01754
|
||||||
|
787|18|F|student|98620
|
||||||
|
788|51|M|administrator|05779
|
||||||
|
789|29|M|other|55420
|
||||||
|
790|27|M|technician|80913
|
||||||
|
791|31|M|educator|20064
|
||||||
|
792|40|M|programmer|12205
|
||||||
|
793|22|M|student|85281
|
||||||
|
794|32|M|educator|57197
|
||||||
|
795|30|M|programmer|08610
|
||||||
|
796|32|F|writer|33755
|
||||||
|
797|44|F|other|62522
|
||||||
|
798|40|F|writer|64131
|
||||||
|
799|49|F|administrator|19716
|
||||||
|
800|25|M|programmer|55337
|
||||||
|
801|22|M|writer|92154
|
||||||
|
802|35|M|administrator|34105
|
||||||
|
803|70|M|administrator|78212
|
||||||
|
804|39|M|educator|61820
|
||||||
|
805|27|F|other|20009
|
||||||
|
806|27|M|marketing|11217
|
||||||
|
807|41|F|healthcare|93555
|
||||||
|
808|45|M|salesman|90016
|
||||||
|
809|50|F|marketing|30803
|
||||||
|
810|55|F|other|80526
|
||||||
|
811|40|F|educator|73013
|
||||||
|
812|22|M|technician|76234
|
||||||
|
813|14|F|student|02136
|
||||||
|
814|30|M|other|12345
|
||||||
|
815|32|M|other|28806
|
||||||
|
816|34|M|other|20755
|
||||||
|
817|19|M|student|60152
|
||||||
|
818|28|M|librarian|27514
|
||||||
|
819|59|M|administrator|40205
|
||||||
|
820|22|M|student|37725
|
||||||
|
821|37|M|engineer|77845
|
||||||
|
822|29|F|librarian|53144
|
||||||
|
823|27|M|artist|50322
|
||||||
|
824|31|M|other|15017
|
||||||
|
825|44|M|engineer|05452
|
||||||
|
826|28|M|artist|77048
|
||||||
|
827|23|F|engineer|80228
|
||||||
|
828|28|M|librarian|85282
|
||||||
|
829|48|M|writer|80209
|
||||||
|
830|46|M|programmer|53066
|
||||||
|
831|21|M|other|33765
|
||||||
|
832|24|M|technician|77042
|
||||||
|
833|34|M|writer|90019
|
||||||
|
834|26|M|other|64153
|
||||||
|
835|44|F|executive|11577
|
||||||
|
836|44|M|artist|10018
|
||||||
|
837|36|F|artist|55409
|
||||||
|
838|23|M|student|01375
|
||||||
|
839|38|F|entertainment|90814
|
||||||
|
840|39|M|artist|55406
|
||||||
|
841|45|M|doctor|47401
|
||||||
|
842|40|M|writer|93055
|
||||||
|
843|35|M|librarian|44212
|
||||||
|
844|22|M|engineer|95662
|
||||||
|
845|64|M|doctor|97405
|
||||||
|
846|27|M|lawyer|47130
|
||||||
|
847|29|M|student|55417
|
||||||
|
848|46|M|engineer|02146
|
||||||
|
849|15|F|student|25652
|
||||||
|
850|34|M|technician|78390
|
||||||
|
851|18|M|other|29646
|
||||||
|
852|46|M|administrator|94086
|
||||||
|
853|49|M|writer|40515
|
||||||
|
854|29|F|student|55408
|
||||||
|
855|53|M|librarian|04988
|
||||||
|
856|43|F|marketing|97215
|
||||||
|
857|35|F|administrator|V1G4L
|
||||||
|
858|63|M|educator|09645
|
||||||
|
859|18|F|other|06492
|
||||||
|
860|70|F|retired|48322
|
||||||
|
861|38|F|student|14085
|
||||||
|
862|25|M|executive|13820
|
||||||
|
863|17|M|student|60089
|
||||||
|
864|27|M|programmer|63021
|
||||||
|
865|25|M|artist|11231
|
||||||
|
866|45|M|other|60302
|
||||||
|
867|24|M|scientist|92507
|
||||||
|
868|21|M|programmer|55303
|
||||||
|
869|30|M|student|10025
|
||||||
|
870|22|M|student|65203
|
||||||
|
871|31|M|executive|44648
|
||||||
|
872|19|F|student|74078
|
||||||
|
873|48|F|administrator|33763
|
||||||
|
874|36|M|scientist|37076
|
||||||
|
875|24|F|student|35802
|
||||||
|
876|41|M|other|20902
|
||||||
|
877|30|M|other|77504
|
||||||
|
878|50|F|educator|98027
|
||||||
|
879|33|F|administrator|55337
|
||||||
|
880|13|M|student|83702
|
||||||
|
881|39|M|marketing|43017
|
||||||
|
882|35|M|engineer|40503
|
||||||
|
883|49|M|librarian|50266
|
||||||
|
884|44|M|engineer|55337
|
||||||
|
885|30|F|other|95316
|
||||||
|
886|20|M|student|61820
|
||||||
|
887|14|F|student|27249
|
||||||
|
888|41|M|scientist|17036
|
||||||
|
889|24|M|technician|78704
|
||||||
|
890|32|M|student|97301
|
||||||
|
891|51|F|administrator|03062
|
||||||
|
892|36|M|other|45243
|
||||||
|
893|25|M|student|95823
|
||||||
|
894|47|M|educator|74075
|
||||||
|
895|31|F|librarian|32301
|
||||||
|
896|28|M|writer|91505
|
||||||
|
897|30|M|other|33484
|
||||||
|
898|23|M|homemaker|61755
|
||||||
|
899|32|M|other|55116
|
||||||
|
900|60|M|retired|18505
|
||||||
|
901|38|M|executive|L1V3W
|
||||||
|
902|45|F|artist|97203
|
||||||
|
903|28|M|educator|20850
|
||||||
|
904|17|F|student|61073
|
||||||
|
905|27|M|other|30350
|
||||||
|
906|45|M|librarian|70124
|
||||||
|
907|25|F|other|80526
|
||||||
|
908|44|F|librarian|68504
|
||||||
|
909|50|F|educator|53171
|
||||||
|
910|28|M|healthcare|29301
|
||||||
|
911|37|F|writer|53210
|
||||||
|
912|51|M|other|06512
|
||||||
|
913|27|M|student|76201
|
||||||
|
914|44|F|other|08105
|
||||||
|
915|50|M|entertainment|60614
|
||||||
|
916|27|M|engineer|N2L5N
|
||||||
|
917|22|F|student|20006
|
||||||
|
918|40|M|scientist|70116
|
||||||
|
919|25|M|other|14216
|
||||||
|
920|30|F|artist|90008
|
||||||
|
921|20|F|student|98801
|
||||||
|
922|29|F|administrator|21114
|
||||||
|
923|21|M|student|E2E3R
|
||||||
|
924|29|M|other|11753
|
||||||
|
925|18|F|salesman|49036
|
||||||
|
926|49|M|entertainment|01701
|
||||||
|
927|23|M|programmer|55428
|
||||||
|
928|21|M|student|55408
|
||||||
|
929|44|M|scientist|53711
|
||||||
|
930|28|F|scientist|07310
|
||||||
|
931|60|M|educator|33556
|
||||||
|
932|58|M|educator|06437
|
||||||
|
933|28|M|student|48105
|
||||||
|
934|61|M|engineer|22902
|
||||||
|
935|42|M|doctor|66221
|
||||||
|
936|24|M|other|32789
|
||||||
|
937|48|M|educator|98072
|
||||||
|
938|38|F|technician|55038
|
||||||
|
939|26|F|student|33319
|
||||||
|
940|32|M|administrator|02215
|
||||||
|
941|20|M|student|97229
|
||||||
|
942|48|F|librarian|78209
|
||||||
|
943|22|M|student|77841
|
||||||
80000
Datasets/ml-100k/u1.base
Normal file
80000
Datasets/ml-100k/u1.base
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20000
Datasets/ml-100k/u1.test
Normal file
20000
Datasets/ml-100k/u1.test
Normal file
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80000
Datasets/ml-100k/u2.base
Normal file
80000
Datasets/ml-100k/u2.base
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20000
Datasets/ml-100k/u2.test
Normal file
20000
Datasets/ml-100k/u2.test
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80000
Datasets/ml-100k/u3.base
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80000
Datasets/ml-100k/u3.base
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20000
Datasets/ml-100k/u3.test
Normal file
20000
Datasets/ml-100k/u3.test
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80000
Datasets/ml-100k/u4.base
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80000
Datasets/ml-100k/u4.base
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20000
Datasets/ml-100k/u4.test
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20000
Datasets/ml-100k/u4.test
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80000
Datasets/ml-100k/u5.base
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80000
Datasets/ml-100k/u5.base
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20000
Datasets/ml-100k/u5.test
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20000
Datasets/ml-100k/u5.test
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90570
Datasets/ml-100k/ua.base
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90570
Datasets/ml-100k/ua.base
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9430
Datasets/ml-100k/ua.test
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9430
Datasets/ml-100k/ua.test
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90570
Datasets/ml-100k/ub.base
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90570
Datasets/ml-100k/ub.base
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9430
Datasets/ml-100k/ub.test
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9430
Datasets/ml-100k/ub.test
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725
P0. Data preparation.ipynb
Normal file
725
P0. Data preparation.ipynb
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1256
P1. Baseline.ipynb
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1256
P1. Baseline.ipynb
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2414
P2. Evaluation.ipynb
Normal file
2414
P2. Evaluation.ipynb
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957
P3. k-nearest neighbours.ipynb
Normal file
957
P3. k-nearest neighbours.ipynb
Normal file
@ -0,0 +1,957 @@
|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# Self made simplified I-KNN"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 3,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import helpers\n",
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"import numpy as np\n",
|
||||||
|
"import scipy.sparse as sparse\n",
|
||||||
|
"from collections import defaultdict\n",
|
||||||
|
"from itertools import chain\n",
|
||||||
|
"import random\n",
|
||||||
|
"\n",
|
||||||
|
"train_read=pd.read_csv('./Datasets/ml-100k/train.csv', sep='\\t', header=None)\n",
|
||||||
|
"test_read=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
|
||||||
|
"train_ui, test_ui, user_code_id, user_id_code, item_code_id, item_id_code = helpers.data_to_csr(train_read, test_read)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 4,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"class IKNN():\n",
|
||||||
|
" \n",
|
||||||
|
" def fit(self, train_ui):\n",
|
||||||
|
" self.train_ui=train_ui\n",
|
||||||
|
" \n",
|
||||||
|
" train_iu=train_ui.transpose()\n",
|
||||||
|
" norms=np.linalg.norm(train_iu.A, axis=1) # here we compute lenth of each item ratings vector\n",
|
||||||
|
" norms=np.vectorize(lambda x: max(x,1))(norms[:,None]) # to avoid dividing by zero\n",
|
||||||
|
"\n",
|
||||||
|
" normalized_train_iu=sparse.csr_matrix(train_iu/norms)\n",
|
||||||
|
"\n",
|
||||||
|
" self.similarity_matrix_ii=normalized_train_iu*normalized_train_iu.transpose()\n",
|
||||||
|
" \n",
|
||||||
|
" self.estimations=np.array(train_ui*self.similarity_matrix_ii/((train_ui>0)*self.similarity_matrix_ii))\n",
|
||||||
|
" \n",
|
||||||
|
" def recommend(self, user_code_id, item_code_id, topK=10):\n",
|
||||||
|
" \n",
|
||||||
|
" top_k = defaultdict(list)\n",
|
||||||
|
" for nb_user, user in enumerate(self.estimations):\n",
|
||||||
|
" \n",
|
||||||
|
" user_rated=self.train_ui.indices[self.train_ui.indptr[nb_user]:self.train_ui.indptr[nb_user+1]]\n",
|
||||||
|
" for item, score in enumerate(user):\n",
|
||||||
|
" if item not in user_rated and not np.isnan(score):\n",
|
||||||
|
" top_k[user_code_id[nb_user]].append((item_code_id[item], score))\n",
|
||||||
|
" result=[]\n",
|
||||||
|
" # Let's choose k best items in the format: (user, item1, score1, item2, score2, ...)\n",
|
||||||
|
" for uid, item_scores in top_k.items():\n",
|
||||||
|
" item_scores.sort(key=lambda x: x[1], reverse=True)\n",
|
||||||
|
" result.append([uid]+list(chain(*item_scores[:topK])))\n",
|
||||||
|
" return result\n",
|
||||||
|
" \n",
|
||||||
|
" def estimate(self, user_code_id, item_code_id, test_ui):\n",
|
||||||
|
" result=[]\n",
|
||||||
|
" for user, item in zip(*test_ui.nonzero()):\n",
|
||||||
|
" result.append([user_code_id[user], item_code_id[item], \n",
|
||||||
|
" self.estimations[user,item] if not np.isnan(self.estimations[user,item]) else 1])\n",
|
||||||
|
" return result"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 5,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"toy train ui:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"array([[3, 4, 0, 0, 5, 0, 0, 4],\n",
|
||||||
|
" [0, 1, 2, 3, 0, 0, 0, 0],\n",
|
||||||
|
" [0, 0, 0, 5, 0, 3, 4, 0]], dtype=int64)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"similarity matrix:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"array([[1. , 0.9701425 , 0. , 0. , 1. ,\n",
|
||||||
|
" 0. , 0. , 1. ],\n",
|
||||||
|
" [0.9701425 , 1. , 0.24253563, 0.12478355, 0.9701425 ,\n",
|
||||||
|
" 0. , 0. , 0.9701425 ],\n",
|
||||||
|
" [0. , 0.24253563, 1. , 0.51449576, 0. ,\n",
|
||||||
|
" 0. , 0. , 0. ],\n",
|
||||||
|
" [0. , 0.12478355, 0.51449576, 1. , 0. ,\n",
|
||||||
|
" 0.85749293, 0.85749293, 0. ],\n",
|
||||||
|
" [1. , 0.9701425 , 0. , 0. , 1. ,\n",
|
||||||
|
" 0. , 0. , 1. ],\n",
|
||||||
|
" [0. , 0. , 0. , 0.85749293, 0. ,\n",
|
||||||
|
" 1. , 1. , 0. ],\n",
|
||||||
|
" [0. , 0. , 0. , 0.85749293, 0. ,\n",
|
||||||
|
" 1. , 1. , 0. ],\n",
|
||||||
|
" [1. , 0.9701425 , 0. , 0. , 1. ,\n",
|
||||||
|
" 0. , 0. , 1. ]])"
|
||||||
|
]
|
||||||
|
},
|
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|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"estimations matrix:\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
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|
"data": {
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||||||
|
"array([[4. , 4. , 4. , 4. , 4. ,\n",
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|
" nan, nan, 4. ],\n",
|
||||||
|
" [1. , 1.35990333, 2.15478388, 2.53390319, 1. ,\n",
|
||||||
|
" 3. , 3. , 1. ],\n",
|
||||||
|
" [ nan, 5. , 5. , 4.05248907, nan,\n",
|
||||||
|
" 3.95012863, 3.95012863, nan]])"
|
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|
]
|
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|
},
|
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|
"metadata": {},
|
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|
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|
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|
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|
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|
"data": {
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"text/plain": [
|
||||||
|
"[[0, 20, 4.0, 30, 4.0],\n",
|
||||||
|
" [10, 50, 3.0, 60, 3.0, 0, 1.0, 40, 1.0, 70, 1.0],\n",
|
||||||
|
" [20, 10, 5.0, 20, 5.0]]"
|
||||||
|
]
|
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|
},
|
||||||
|
"execution_count": 5,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# toy example\n",
|
||||||
|
"toy_train_read=pd.read_csv('./Datasets/toy-example/train.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
|
||||||
|
"toy_test_read=pd.read_csv('./Datasets/toy-example/test.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
|
||||||
|
"\n",
|
||||||
|
"toy_train_ui, toy_test_ui, toy_user_code_id, toy_user_id_code, \\\n",
|
||||||
|
"toy_item_code_id, toy_item_id_code = helpers.data_to_csr(toy_train_read, toy_test_read)\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"model=IKNN()\n",
|
||||||
|
"model.fit(toy_train_ui)\n",
|
||||||
|
"\n",
|
||||||
|
"print('toy train ui:')\n",
|
||||||
|
"display(toy_train_ui.A)\n",
|
||||||
|
"\n",
|
||||||
|
"print('similarity matrix:')\n",
|
||||||
|
"display(model.similarity_matrix_ii.A)\n",
|
||||||
|
"\n",
|
||||||
|
"print('estimations matrix:')\n",
|
||||||
|
"display(model.estimations)\n",
|
||||||
|
"\n",
|
||||||
|
"model.recommend(toy_user_code_id, toy_item_code_id)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 6,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"model=IKNN()\n",
|
||||||
|
"model.fit(train_ui)\n",
|
||||||
|
"\n",
|
||||||
|
"top_n=pd.DataFrame(model.recommend(user_code_id, item_code_id, topK=10))\n",
|
||||||
|
"\n",
|
||||||
|
"top_n.to_csv('Recommendations generated/ml-100k/Self_IKNN_reco.csv', index=False, header=False)\n",
|
||||||
|
"\n",
|
||||||
|
"estimations=pd.DataFrame(model.estimate(user_code_id, item_code_id, test_ui))\n",
|
||||||
|
"estimations.to_csv('Recommendations generated/ml-100k/Self_IKNN_estimations.csv', index=False, header=False)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 7,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
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|
{
|
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|
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|
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|
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|
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|
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||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>RMSE</th>\n",
|
||||||
|
" <th>MAE</th>\n",
|
||||||
|
" <th>precision</th>\n",
|
||||||
|
" <th>recall</th>\n",
|
||||||
|
" <th>F_1</th>\n",
|
||||||
|
" <th>F_05</th>\n",
|
||||||
|
" <th>precision_super</th>\n",
|
||||||
|
" <th>recall_super</th>\n",
|
||||||
|
" <th>NDCG</th>\n",
|
||||||
|
" <th>mAP</th>\n",
|
||||||
|
" <th>MRR</th>\n",
|
||||||
|
" <th>LAUC</th>\n",
|
||||||
|
" <th>HR</th>\n",
|
||||||
|
" <th>Reco in test</th>\n",
|
||||||
|
" <th>Test coverage</th>\n",
|
||||||
|
" <th>Shannon</th>\n",
|
||||||
|
" <th>Gini</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>1.018363</td>\n",
|
||||||
|
" <td>0.808793</td>\n",
|
||||||
|
" <td>0.000318</td>\n",
|
||||||
|
" <td>0.000108</td>\n",
|
||||||
|
" <td>0.00014</td>\n",
|
||||||
|
" <td>0.000189</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>0.0</td>\n",
|
||||||
|
" <td>0.000214</td>\n",
|
||||||
|
" <td>0.000037</td>\n",
|
||||||
|
" <td>0.000368</td>\n",
|
||||||
|
" <td>0.496391</td>\n",
|
||||||
|
" <td>0.003181</td>\n",
|
||||||
|
" <td>0.392153</td>\n",
|
||||||
|
" <td>0.11544</td>\n",
|
||||||
|
" <td>4.174741</td>\n",
|
||||||
|
" <td>0.965327</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" RMSE MAE precision recall F_1 F_05 \\\n",
|
||||||
|
"0 1.018363 0.808793 0.000318 0.000108 0.00014 0.000189 \n",
|
||||||
|
"\n",
|
||||||
|
" precision_super recall_super NDCG mAP MRR LAUC \\\n",
|
||||||
|
"0 0.0 0.0 0.000214 0.000037 0.000368 0.496391 \n",
|
||||||
|
"\n",
|
||||||
|
" HR Reco in test Test coverage Shannon Gini \n",
|
||||||
|
"0 0.003181 0.392153 0.11544 4.174741 0.965327 "
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 7,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"import evaluation_measures as ev\n",
|
||||||
|
"estimations_df=pd.read_csv('Recommendations generated/ml-100k/Self_IKNN_estimations.csv', header=None)\n",
|
||||||
|
"reco=np.loadtxt('Recommendations generated/ml-100k/Self_IKNN_reco.csv', delimiter=',')\n",
|
||||||
|
"\n",
|
||||||
|
"ev.evaluate(test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None),\n",
|
||||||
|
" estimations_df=estimations_df, \n",
|
||||||
|
" reco=reco,\n",
|
||||||
|
" super_reactions=[4,5])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 8,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
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||||||
|
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|
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|
" vertical-align: middle;\n",
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|
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|
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|
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" .dataframe tbody tr th {\n",
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|
" vertical-align: top;\n",
|
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|
" }\n",
|
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|
"\n",
|
||||||
|
" .dataframe thead th {\n",
|
||||||
|
" text-align: right;\n",
|
||||||
|
" }\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>Model</th>\n",
|
||||||
|
" <th>RMSE</th>\n",
|
||||||
|
" <th>MAE</th>\n",
|
||||||
|
" <th>precision</th>\n",
|
||||||
|
" <th>recall</th>\n",
|
||||||
|
" <th>F_1</th>\n",
|
||||||
|
" <th>F_05</th>\n",
|
||||||
|
" <th>precision_super</th>\n",
|
||||||
|
" <th>recall_super</th>\n",
|
||||||
|
" <th>NDCG</th>\n",
|
||||||
|
" <th>mAP</th>\n",
|
||||||
|
" <th>MRR</th>\n",
|
||||||
|
" <th>LAUC</th>\n",
|
||||||
|
" <th>HR</th>\n",
|
||||||
|
" <th>Reco in test</th>\n",
|
||||||
|
" <th>Test coverage</th>\n",
|
||||||
|
" <th>Shannon</th>\n",
|
||||||
|
" <th>Gini</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Self_RP3Beta</td>\n",
|
||||||
|
" <td>3.702446</td>\n",
|
||||||
|
" <td>3.527273</td>\n",
|
||||||
|
" <td>0.282185</td>\n",
|
||||||
|
" <td>0.192092</td>\n",
|
||||||
|
" <td>0.186749</td>\n",
|
||||||
|
" <td>0.216980</td>\n",
|
||||||
|
" <td>0.204185</td>\n",
|
||||||
|
" <td>0.240096</td>\n",
|
||||||
|
" <td>0.339114</td>\n",
|
||||||
|
" <td>0.204905</td>\n",
|
||||||
|
" <td>0.572157</td>\n",
|
||||||
|
" <td>0.593544</td>\n",
|
||||||
|
" <td>0.875928</td>\n",
|
||||||
|
" <td>1.000000</td>\n",
|
||||||
|
" <td>0.077201</td>\n",
|
||||||
|
" <td>3.875892</td>\n",
|
||||||
|
" <td>0.974947</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Self_TopPop</td>\n",
|
||||||
|
" <td>2.508258</td>\n",
|
||||||
|
" <td>2.217909</td>\n",
|
||||||
|
" <td>0.188865</td>\n",
|
||||||
|
" <td>0.116919</td>\n",
|
||||||
|
" <td>0.118732</td>\n",
|
||||||
|
" <td>0.141584</td>\n",
|
||||||
|
" <td>0.130472</td>\n",
|
||||||
|
" <td>0.137473</td>\n",
|
||||||
|
" <td>0.214651</td>\n",
|
||||||
|
" <td>0.111707</td>\n",
|
||||||
|
" <td>0.400939</td>\n",
|
||||||
|
" <td>0.555546</td>\n",
|
||||||
|
" <td>0.765642</td>\n",
|
||||||
|
" <td>1.000000</td>\n",
|
||||||
|
" <td>0.038961</td>\n",
|
||||||
|
" <td>3.159079</td>\n",
|
||||||
|
" <td>0.987317</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Ready_SVD</td>\n",
|
||||||
|
" <td>0.952784</td>\n",
|
||||||
|
" <td>0.750597</td>\n",
|
||||||
|
" <td>0.095228</td>\n",
|
||||||
|
" <td>0.047497</td>\n",
|
||||||
|
" <td>0.053142</td>\n",
|
||||||
|
" <td>0.067082</td>\n",
|
||||||
|
" <td>0.084871</td>\n",
|
||||||
|
" <td>0.076457</td>\n",
|
||||||
|
" <td>0.109075</td>\n",
|
||||||
|
" <td>0.050124</td>\n",
|
||||||
|
" <td>0.241366</td>\n",
|
||||||
|
" <td>0.520459</td>\n",
|
||||||
|
" <td>0.499470</td>\n",
|
||||||
|
" <td>0.992047</td>\n",
|
||||||
|
" <td>0.217893</td>\n",
|
||||||
|
" <td>4.405246</td>\n",
|
||||||
|
" <td>0.953484</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Self_SVDBaseline</td>\n",
|
||||||
|
" <td>0.930321</td>\n",
|
||||||
|
" <td>0.734643</td>\n",
|
||||||
|
" <td>0.092683</td>\n",
|
||||||
|
" <td>0.042046</td>\n",
|
||||||
|
" <td>0.048568</td>\n",
|
||||||
|
" <td>0.063218</td>\n",
|
||||||
|
" <td>0.082940</td>\n",
|
||||||
|
" <td>0.068730</td>\n",
|
||||||
|
" <td>0.098937</td>\n",
|
||||||
|
" <td>0.044405</td>\n",
|
||||||
|
" <td>0.203936</td>\n",
|
||||||
|
" <td>0.517696</td>\n",
|
||||||
|
" <td>0.469777</td>\n",
|
||||||
|
" <td>1.000000</td>\n",
|
||||||
|
" <td>0.058442</td>\n",
|
||||||
|
" <td>3.085857</td>\n",
|
||||||
|
" <td>0.988824</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Ready_SVDBiased</td>\n",
|
||||||
|
" <td>0.940375</td>\n",
|
||||||
|
" <td>0.742264</td>\n",
|
||||||
|
" <td>0.092153</td>\n",
|
||||||
|
" <td>0.039645</td>\n",
|
||||||
|
" <td>0.046804</td>\n",
|
||||||
|
" <td>0.061886</td>\n",
|
||||||
|
" <td>0.079399</td>\n",
|
||||||
|
" <td>0.055967</td>\n",
|
||||||
|
" <td>0.102017</td>\n",
|
||||||
|
" <td>0.047972</td>\n",
|
||||||
|
" <td>0.216876</td>\n",
|
||||||
|
" <td>0.516515</td>\n",
|
||||||
|
" <td>0.441145</td>\n",
|
||||||
|
" <td>0.997455</td>\n",
|
||||||
|
" <td>0.167388</td>\n",
|
||||||
|
" <td>4.235348</td>\n",
|
||||||
|
" <td>0.962085</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Ready_Baseline</td>\n",
|
||||||
|
" <td>0.949459</td>\n",
|
||||||
|
" <td>0.752487</td>\n",
|
||||||
|
" <td>0.091410</td>\n",
|
||||||
|
" <td>0.037652</td>\n",
|
||||||
|
" <td>0.046030</td>\n",
|
||||||
|
" <td>0.061286</td>\n",
|
||||||
|
" <td>0.079614</td>\n",
|
||||||
|
" <td>0.056463</td>\n",
|
||||||
|
" <td>0.095957</td>\n",
|
||||||
|
" <td>0.043178</td>\n",
|
||||||
|
" <td>0.198193</td>\n",
|
||||||
|
" <td>0.515501</td>\n",
|
||||||
|
" <td>0.437964</td>\n",
|
||||||
|
" <td>1.000000</td>\n",
|
||||||
|
" <td>0.033911</td>\n",
|
||||||
|
" <td>2.836513</td>\n",
|
||||||
|
" <td>0.991139</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Self_SVD</td>\n",
|
||||||
|
" <td>0.939326</td>\n",
|
||||||
|
" <td>0.740022</td>\n",
|
||||||
|
" <td>0.074549</td>\n",
|
||||||
|
" <td>0.031755</td>\n",
|
||||||
|
" <td>0.038425</td>\n",
|
||||||
|
" <td>0.050562</td>\n",
|
||||||
|
" <td>0.065665</td>\n",
|
||||||
|
" <td>0.050602</td>\n",
|
||||||
|
" <td>0.077117</td>\n",
|
||||||
|
" <td>0.031574</td>\n",
|
||||||
|
" <td>0.165509</td>\n",
|
||||||
|
" <td>0.512485</td>\n",
|
||||||
|
" <td>0.414634</td>\n",
|
||||||
|
" <td>0.981866</td>\n",
|
||||||
|
" <td>0.080087</td>\n",
|
||||||
|
" <td>3.858982</td>\n",
|
||||||
|
" <td>0.975271</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Self_GlobalAvg</td>\n",
|
||||||
|
" <td>1.125760</td>\n",
|
||||||
|
" <td>0.943534</td>\n",
|
||||||
|
" <td>0.061188</td>\n",
|
||||||
|
" <td>0.025968</td>\n",
|
||||||
|
" <td>0.031383</td>\n",
|
||||||
|
" <td>0.041343</td>\n",
|
||||||
|
" <td>0.040558</td>\n",
|
||||||
|
" <td>0.032107</td>\n",
|
||||||
|
" <td>0.067695</td>\n",
|
||||||
|
" <td>0.027470</td>\n",
|
||||||
|
" <td>0.171187</td>\n",
|
||||||
|
" <td>0.509546</td>\n",
|
||||||
|
" <td>0.384942</td>\n",
|
||||||
|
" <td>1.000000</td>\n",
|
||||||
|
" <td>0.025974</td>\n",
|
||||||
|
" <td>2.711772</td>\n",
|
||||||
|
" <td>0.992003</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Ready_Random</td>\n",
|
||||||
|
" <td>1.518551</td>\n",
|
||||||
|
" <td>1.218784</td>\n",
|
||||||
|
" <td>0.050583</td>\n",
|
||||||
|
" <td>0.024085</td>\n",
|
||||||
|
" <td>0.027323</td>\n",
|
||||||
|
" <td>0.034826</td>\n",
|
||||||
|
" <td>0.031223</td>\n",
|
||||||
|
" <td>0.026436</td>\n",
|
||||||
|
" <td>0.054902</td>\n",
|
||||||
|
" <td>0.020652</td>\n",
|
||||||
|
" <td>0.137928</td>\n",
|
||||||
|
" <td>0.508570</td>\n",
|
||||||
|
" <td>0.353128</td>\n",
|
||||||
|
" <td>0.987699</td>\n",
|
||||||
|
" <td>0.183261</td>\n",
|
||||||
|
" <td>5.093805</td>\n",
|
||||||
|
" <td>0.908215</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Ready_I-KNN</td>\n",
|
||||||
|
" <td>1.030386</td>\n",
|
||||||
|
" <td>0.813067</td>\n",
|
||||||
|
" <td>0.026087</td>\n",
|
||||||
|
" <td>0.006908</td>\n",
|
||||||
|
" <td>0.010593</td>\n",
|
||||||
|
" <td>0.016046</td>\n",
|
||||||
|
" <td>0.021137</td>\n",
|
||||||
|
" <td>0.009522</td>\n",
|
||||||
|
" <td>0.024214</td>\n",
|
||||||
|
" <td>0.008958</td>\n",
|
||||||
|
" <td>0.048068</td>\n",
|
||||||
|
" <td>0.499885</td>\n",
|
||||||
|
" <td>0.154825</td>\n",
|
||||||
|
" <td>0.402333</td>\n",
|
||||||
|
" <td>0.434343</td>\n",
|
||||||
|
" <td>5.133650</td>\n",
|
||||||
|
" <td>0.877999</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Ready_U-KNNBaseline</td>\n",
|
||||||
|
" <td>0.935327</td>\n",
|
||||||
|
" <td>0.737424</td>\n",
|
||||||
|
" <td>0.002545</td>\n",
|
||||||
|
" <td>0.000755</td>\n",
|
||||||
|
" <td>0.001105</td>\n",
|
||||||
|
" <td>0.001602</td>\n",
|
||||||
|
" <td>0.002253</td>\n",
|
||||||
|
" <td>0.000930</td>\n",
|
||||||
|
" <td>0.003444</td>\n",
|
||||||
|
" <td>0.001362</td>\n",
|
||||||
|
" <td>0.011760</td>\n",
|
||||||
|
" <td>0.496724</td>\n",
|
||||||
|
" <td>0.021209</td>\n",
|
||||||
|
" <td>0.482821</td>\n",
|
||||||
|
" <td>0.059885</td>\n",
|
||||||
|
" <td>2.232578</td>\n",
|
||||||
|
" <td>0.994487</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Ready_I-KNNBaseline</td>\n",
|
||||||
|
" <td>0.935327</td>\n",
|
||||||
|
" <td>0.737424</td>\n",
|
||||||
|
" <td>0.002545</td>\n",
|
||||||
|
" <td>0.000755</td>\n",
|
||||||
|
" <td>0.001105</td>\n",
|
||||||
|
" <td>0.001602</td>\n",
|
||||||
|
" <td>0.002253</td>\n",
|
||||||
|
" <td>0.000930</td>\n",
|
||||||
|
" <td>0.003444</td>\n",
|
||||||
|
" <td>0.001362</td>\n",
|
||||||
|
" <td>0.011760</td>\n",
|
||||||
|
" <td>0.496724</td>\n",
|
||||||
|
" <td>0.021209</td>\n",
|
||||||
|
" <td>0.482821</td>\n",
|
||||||
|
" <td>0.059885</td>\n",
|
||||||
|
" <td>2.232578</td>\n",
|
||||||
|
" <td>0.994487</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Ready_U-KNN</td>\n",
|
||||||
|
" <td>1.023495</td>\n",
|
||||||
|
" <td>0.807913</td>\n",
|
||||||
|
" <td>0.000742</td>\n",
|
||||||
|
" <td>0.000205</td>\n",
|
||||||
|
" <td>0.000305</td>\n",
|
||||||
|
" <td>0.000449</td>\n",
|
||||||
|
" <td>0.000536</td>\n",
|
||||||
|
" <td>0.000198</td>\n",
|
||||||
|
" <td>0.000845</td>\n",
|
||||||
|
" <td>0.000274</td>\n",
|
||||||
|
" <td>0.002744</td>\n",
|
||||||
|
" <td>0.496441</td>\n",
|
||||||
|
" <td>0.007423</td>\n",
|
||||||
|
" <td>0.602121</td>\n",
|
||||||
|
" <td>0.010823</td>\n",
|
||||||
|
" <td>2.089186</td>\n",
|
||||||
|
" <td>0.995706</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Self_TopRated</td>\n",
|
||||||
|
" <td>1.033085</td>\n",
|
||||||
|
" <td>0.822057</td>\n",
|
||||||
|
" <td>0.000954</td>\n",
|
||||||
|
" <td>0.000188</td>\n",
|
||||||
|
" <td>0.000298</td>\n",
|
||||||
|
" <td>0.000481</td>\n",
|
||||||
|
" <td>0.000644</td>\n",
|
||||||
|
" <td>0.000223</td>\n",
|
||||||
|
" <td>0.001043</td>\n",
|
||||||
|
" <td>0.000335</td>\n",
|
||||||
|
" <td>0.003348</td>\n",
|
||||||
|
" <td>0.496433</td>\n",
|
||||||
|
" <td>0.009544</td>\n",
|
||||||
|
" <td>0.699046</td>\n",
|
||||||
|
" <td>0.005051</td>\n",
|
||||||
|
" <td>1.945910</td>\n",
|
||||||
|
" <td>0.995669</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Self_BaselineUI</td>\n",
|
||||||
|
" <td>0.967585</td>\n",
|
||||||
|
" <td>0.762740</td>\n",
|
||||||
|
" <td>0.000954</td>\n",
|
||||||
|
" <td>0.000170</td>\n",
|
||||||
|
" <td>0.000278</td>\n",
|
||||||
|
" <td>0.000463</td>\n",
|
||||||
|
" <td>0.000644</td>\n",
|
||||||
|
" <td>0.000189</td>\n",
|
||||||
|
" <td>0.000752</td>\n",
|
||||||
|
" <td>0.000168</td>\n",
|
||||||
|
" <td>0.001677</td>\n",
|
||||||
|
" <td>0.496424</td>\n",
|
||||||
|
" <td>0.009544</td>\n",
|
||||||
|
" <td>0.600530</td>\n",
|
||||||
|
" <td>0.005051</td>\n",
|
||||||
|
" <td>1.803126</td>\n",
|
||||||
|
" <td>0.996380</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>Self_IKNN</td>\n",
|
||||||
|
" <td>1.018363</td>\n",
|
||||||
|
" <td>0.808793</td>\n",
|
||||||
|
" <td>0.000318</td>\n",
|
||||||
|
" <td>0.000108</td>\n",
|
||||||
|
" <td>0.000140</td>\n",
|
||||||
|
" <td>0.000189</td>\n",
|
||||||
|
" <td>0.000000</td>\n",
|
||||||
|
" <td>0.000000</td>\n",
|
||||||
|
" <td>0.000214</td>\n",
|
||||||
|
" <td>0.000037</td>\n",
|
||||||
|
" <td>0.000368</td>\n",
|
||||||
|
" <td>0.496391</td>\n",
|
||||||
|
" <td>0.003181</td>\n",
|
||||||
|
" <td>0.392153</td>\n",
|
||||||
|
" <td>0.115440</td>\n",
|
||||||
|
" <td>4.174741</td>\n",
|
||||||
|
" <td>0.965327</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" Model RMSE MAE precision recall F_1 \\\n",
|
||||||
|
"0 Self_RP3Beta 3.702446 3.527273 0.282185 0.192092 0.186749 \n",
|
||||||
|
"0 Self_TopPop 2.508258 2.217909 0.188865 0.116919 0.118732 \n",
|
||||||
|
"0 Ready_SVD 0.952784 0.750597 0.095228 0.047497 0.053142 \n",
|
||||||
|
"0 Self_SVDBaseline 0.930321 0.734643 0.092683 0.042046 0.048568 \n",
|
||||||
|
"0 Ready_SVDBiased 0.940375 0.742264 0.092153 0.039645 0.046804 \n",
|
||||||
|
"0 Ready_Baseline 0.949459 0.752487 0.091410 0.037652 0.046030 \n",
|
||||||
|
"0 Self_SVD 0.939326 0.740022 0.074549 0.031755 0.038425 \n",
|
||||||
|
"0 Self_GlobalAvg 1.125760 0.943534 0.061188 0.025968 0.031383 \n",
|
||||||
|
"0 Ready_Random 1.518551 1.218784 0.050583 0.024085 0.027323 \n",
|
||||||
|
"0 Ready_I-KNN 1.030386 0.813067 0.026087 0.006908 0.010593 \n",
|
||||||
|
"0 Ready_U-KNNBaseline 0.935327 0.737424 0.002545 0.000755 0.001105 \n",
|
||||||
|
"0 Ready_I-KNNBaseline 0.935327 0.737424 0.002545 0.000755 0.001105 \n",
|
||||||
|
"0 Ready_U-KNN 1.023495 0.807913 0.000742 0.000205 0.000305 \n",
|
||||||
|
"0 Self_TopRated 1.033085 0.822057 0.000954 0.000188 0.000298 \n",
|
||||||
|
"0 Self_BaselineUI 0.967585 0.762740 0.000954 0.000170 0.000278 \n",
|
||||||
|
"0 Self_IKNN 1.018363 0.808793 0.000318 0.000108 0.000140 \n",
|
||||||
|
"\n",
|
||||||
|
" F_05 precision_super recall_super NDCG mAP MRR \\\n",
|
||||||
|
"0 0.216980 0.204185 0.240096 0.339114 0.204905 0.572157 \n",
|
||||||
|
"0 0.141584 0.130472 0.137473 0.214651 0.111707 0.400939 \n",
|
||||||
|
"0 0.067082 0.084871 0.076457 0.109075 0.050124 0.241366 \n",
|
||||||
|
"0 0.063218 0.082940 0.068730 0.098937 0.044405 0.203936 \n",
|
||||||
|
"0 0.061886 0.079399 0.055967 0.102017 0.047972 0.216876 \n",
|
||||||
|
"0 0.061286 0.079614 0.056463 0.095957 0.043178 0.198193 \n",
|
||||||
|
"0 0.050562 0.065665 0.050602 0.077117 0.031574 0.165509 \n",
|
||||||
|
"0 0.041343 0.040558 0.032107 0.067695 0.027470 0.171187 \n",
|
||||||
|
"0 0.034826 0.031223 0.026436 0.054902 0.020652 0.137928 \n",
|
||||||
|
"0 0.016046 0.021137 0.009522 0.024214 0.008958 0.048068 \n",
|
||||||
|
"0 0.001602 0.002253 0.000930 0.003444 0.001362 0.011760 \n",
|
||||||
|
"0 0.001602 0.002253 0.000930 0.003444 0.001362 0.011760 \n",
|
||||||
|
"0 0.000449 0.000536 0.000198 0.000845 0.000274 0.002744 \n",
|
||||||
|
"0 0.000481 0.000644 0.000223 0.001043 0.000335 0.003348 \n",
|
||||||
|
"0 0.000463 0.000644 0.000189 0.000752 0.000168 0.001677 \n",
|
||||||
|
"0 0.000189 0.000000 0.000000 0.000214 0.000037 0.000368 \n",
|
||||||
|
"\n",
|
||||||
|
" LAUC HR Reco in test Test coverage Shannon Gini \n",
|
||||||
|
"0 0.593544 0.875928 1.000000 0.077201 3.875892 0.974947 \n",
|
||||||
|
"0 0.555546 0.765642 1.000000 0.038961 3.159079 0.987317 \n",
|
||||||
|
"0 0.520459 0.499470 0.992047 0.217893 4.405246 0.953484 \n",
|
||||||
|
"0 0.517696 0.469777 1.000000 0.058442 3.085857 0.988824 \n",
|
||||||
|
"0 0.516515 0.441145 0.997455 0.167388 4.235348 0.962085 \n",
|
||||||
|
"0 0.515501 0.437964 1.000000 0.033911 2.836513 0.991139 \n",
|
||||||
|
"0 0.512485 0.414634 0.981866 0.080087 3.858982 0.975271 \n",
|
||||||
|
"0 0.509546 0.384942 1.000000 0.025974 2.711772 0.992003 \n",
|
||||||
|
"0 0.508570 0.353128 0.987699 0.183261 5.093805 0.908215 \n",
|
||||||
|
"0 0.499885 0.154825 0.402333 0.434343 5.133650 0.877999 \n",
|
||||||
|
"0 0.496724 0.021209 0.482821 0.059885 2.232578 0.994487 \n",
|
||||||
|
"0 0.496724 0.021209 0.482821 0.059885 2.232578 0.994487 \n",
|
||||||
|
"0 0.496441 0.007423 0.602121 0.010823 2.089186 0.995706 \n",
|
||||||
|
"0 0.496433 0.009544 0.699046 0.005051 1.945910 0.995669 \n",
|
||||||
|
"0 0.496424 0.009544 0.600530 0.005051 1.803126 0.996380 \n",
|
||||||
|
"0 0.496391 0.003181 0.392153 0.115440 4.174741 0.965327 "
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 8,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"import imp\n",
|
||||||
|
"imp.reload(ev)\n",
|
||||||
|
"\n",
|
||||||
|
"import evaluation_measures as ev\n",
|
||||||
|
"dir_path=\"Recommendations generated/ml-100k/\"\n",
|
||||||
|
"super_reactions=[4,5]\n",
|
||||||
|
"test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
|
||||||
|
"\n",
|
||||||
|
"ev.evaluate_all(test, dir_path, super_reactions)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# Ready-made KNNs - Surprise implementation"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### I-KNN - basic"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 9,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Computing the cosine similarity matrix...\n",
|
||||||
|
"Done computing similarity matrix.\n",
|
||||||
|
"Generating predictions...\n",
|
||||||
|
"Generating top N recommendations...\n",
|
||||||
|
"Generating predictions...\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"import helpers\n",
|
||||||
|
"import surprise as sp\n",
|
||||||
|
"import imp\n",
|
||||||
|
"imp.reload(helpers)\n",
|
||||||
|
"\n",
|
||||||
|
"sim_options = {'name': 'cosine',\n",
|
||||||
|
" 'user_based': False} # compute similarities between items\n",
|
||||||
|
"algo = sp.KNNBasic(sim_options=sim_options)\n",
|
||||||
|
"\n",
|
||||||
|
"helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_I-KNN_reco.csv',\n",
|
||||||
|
" estimations_path='Recommendations generated/ml-100k/Ready_Baseline_I-KNN_estimations.csv')"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### U-KNN - basic"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 10,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Computing the cosine similarity matrix...\n",
|
||||||
|
"Done computing similarity matrix.\n",
|
||||||
|
"Generating predictions...\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"ename": "KeyboardInterrupt",
|
||||||
|
"evalue": "",
|
||||||
|
"output_type": "error",
|
||||||
|
"traceback": [
|
||||||
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||||
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||||
|
"\u001b[0;32m<ipython-input-10-dd4f59625a08>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_U-KNN_reco.csv',\n\u001b[0;32m---> 11\u001b[0;31m estimations_path='Recommendations generated/ml-100k/Ready_Baseline_U-KNN_estimations.csv')\n\u001b[0m",
|
||||||
|
"\u001b[0;32m/mnt/c/Users/rkwie/Repositories/Warsztaty z uczenia maszynowego - systemy rekomendacyjne/helpers.py\u001b[0m in \u001b[0;36mready_made\u001b[0;34m(algo, reco_path, estimations_path)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0mantitrainset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_anti_testset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# We want to predict ratings of pairs (user, item) which are not in train set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Generating predictions...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mantitrainset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Generating top N recommendations...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0mtop_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_top_n\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;32m~/.local/lib/python3.6/site-packages/surprise/prediction_algorithms/algo_base.py\u001b[0m in \u001b[0;36mtest\u001b[0;34m(self, testset, verbose)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0mr_ui_trans\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m verbose=verbose)\n\u001b[0;32m--> 167\u001b[0;31m for (uid, iid, r_ui_trans) in testset]\n\u001b[0m\u001b[1;32m 168\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;32m~/.local/lib/python3.6/site-packages/surprise/prediction_algorithms/algo_base.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[0mr_ui_trans\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 166\u001b[0m verbose=verbose)\n\u001b[0;32m--> 167\u001b[0;31m for (uid, iid, r_ui_trans) in testset]\n\u001b[0m\u001b[1;32m 168\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;32m~/.local/lib/python3.6/site-packages/surprise/prediction_algorithms/algo_base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, uid, iid, r_ui, clip, verbose)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mdetails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0mest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miuid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miiid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;31m# If the details dict was also returned\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;32m~/.local/lib/python3.6/site-packages/surprise/prediction_algorithms/knns.py\u001b[0m in \u001b[0;36mestimate\u001b[0;34m(self, u, i)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0mneighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m \u001b[0mk_neighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheapq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlargest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneighbors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;31m# compute weighted average\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;32m/usr/lib/python3.6/heapq.py\u001b[0m in \u001b[0;36mnlargest\u001b[0;34m(n, iterable, key)\u001b[0m\n\u001b[1;32m 567\u001b[0m \u001b[0;31m# General case, slowest method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 568\u001b[0m \u001b[0mit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 570\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 571\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;32m/usr/lib/python3.6/heapq.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 567\u001b[0m \u001b[0;31m# General case, slowest method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 568\u001b[0m \u001b[0mit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 570\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 571\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;32m~/.local/lib/python3.6/site-packages/surprise/prediction_algorithms/knns.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0mneighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m \u001b[0mk_neighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheapq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlargest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneighbors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;31m# compute weighted average\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"import helpers\n",
|
||||||
|
"import surprise as sp\n",
|
||||||
|
"import imp\n",
|
||||||
|
"imp.reload(helpers)\n",
|
||||||
|
"\n",
|
||||||
|
"sim_options = {'name': 'cosine',\n",
|
||||||
|
" 'user_based': True} # compute similarities between users\n",
|
||||||
|
"algo = sp.KNNBasic(sim_options=sim_options)\n",
|
||||||
|
"\n",
|
||||||
|
"helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_U-KNN_reco.csv',\n",
|
||||||
|
" estimations_path='Recommendations generated/ml-100k/Ready_Baseline_U-KNN_estimations.csv')"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### I-KNN - on top baseline"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import helpers\n",
|
||||||
|
"import surprise as sp\n",
|
||||||
|
"import imp\n",
|
||||||
|
"imp.reload(helpers)\n",
|
||||||
|
"\n",
|
||||||
|
"sim_options = {'name': 'cosine',\n",
|
||||||
|
" 'user_based': False} # compute similarities between items\n",
|
||||||
|
"algo = sp.KNNBaseline()\n",
|
||||||
|
"\n",
|
||||||
|
"helpers.ready_made(algo, reco_path='Recommendations generated/ml-100k/Ready_I-KNNBaseline_reco.csv',\n",
|
||||||
|
" estimations_path='Recommendations generated/ml-100k/Ready_I-KNNBaseline_estimations.csv')"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# project task 4: use a version of your choice of Surprise KNNalgorithm"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 12,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# read the docs and try to find best parameter configuration (let say in terms of RMSE)\n",
|
||||||
|
"# https://surprise.readthedocs.io/en/stable/knn_inspired.html##surprise.prediction_algorithms.knns.KNNBaseline\n",
|
||||||
|
"# the solution here can be similar to examples above\n",
|
||||||
|
"# please save the output in 'Recommendations generated/ml-100k/Self_KNNSurprisetask_reco.csv' and\n",
|
||||||
|
"# 'Recommendations generated/ml-100k/Self_KNNSurprisetask_estimations.csv'"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.6.9"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 4
|
||||||
|
}
|
||||||
80
P4. Appendix - embeddings in high demensional spaces.ipynb
Normal file
80
P4. Appendix - embeddings in high demensional spaces.ipynb
Normal file
@ -0,0 +1,80 @@
|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 4,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"['dimensions: 1, cases when observation is the nearest: 0.0%',\n",
|
||||||
|
" 'dimensions: 2, cases when observation is the nearest: 0.0%',\n",
|
||||||
|
" 'dimensions: 3, cases when observation is the nearest: 0.0%',\n",
|
||||||
|
" 'dimensions: 10, cases when observation is the nearest: 10.0%',\n",
|
||||||
|
" 'dimensions: 20, cases when observation is the nearest: 61.0%',\n",
|
||||||
|
" 'dimensions: 30, cases when observation is the nearest: 96.0%',\n",
|
||||||
|
" 'dimensions: 40, cases when observation is the nearest: 98.0%',\n",
|
||||||
|
" 'dimensions: 50, cases when observation is the nearest: 100.0%',\n",
|
||||||
|
" 'dimensions: 60, cases when observation is the nearest: 100.0%',\n",
|
||||||
|
" 'dimensions: 70, cases when observation is the nearest: 100.0%',\n",
|
||||||
|
" 'dimensions: 80, cases when observation is the nearest: 100.0%',\n",
|
||||||
|
" 'dimensions: 90, cases when observation is the nearest: 100.0%']"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 4,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"import numpy as np\n",
|
||||||
|
"import pandas as pd\n",
|
||||||
|
"import random\n",
|
||||||
|
"from numpy.linalg import norm\n",
|
||||||
|
"\n",
|
||||||
|
"dimensions=[1,2,3]+[10*i for i in range(1,10)]\n",
|
||||||
|
"nb_vectors=10000\n",
|
||||||
|
"trials=100\n",
|
||||||
|
"k=1 # by setting k=1 we want to check how often the closest vector to the avarage of 2 random vectors is one of these 2 vectors\n",
|
||||||
|
"\n",
|
||||||
|
"result=[]\n",
|
||||||
|
"for dimension in dimensions:\n",
|
||||||
|
" vectors=np.random.normal(0,1,size=(nb_vectors, dimension))\n",
|
||||||
|
" successes=0\n",
|
||||||
|
" for i in range(trials):\n",
|
||||||
|
" i1,i2=random.sample(range(nb_vectors),2)\n",
|
||||||
|
" target=(vectors[i1]+vectors[i2])/2\n",
|
||||||
|
"\n",
|
||||||
|
" distances=pd.DataFrame(enumerate(np.dot(target, vectors.transpose())/norm(target)/norm(vectors.transpose(), axis=0)))\n",
|
||||||
|
" distances=distances.sort_values(by=[1], ascending=False)\n",
|
||||||
|
" if (i1 in (list(distances[0][:k]))) | (i2 in (list(distances[0][:k]))):\n",
|
||||||
|
" successes+=1\n",
|
||||||
|
" result.append(successes/trials)\n",
|
||||||
|
" \n",
|
||||||
|
"[f'dimensions: {i}, cases when observation is the nearest: {100*round(j,3)}%' for i,j in zip(dimensions, result)]"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.6.9"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
1641
P4. Matrix Factorization.ipynb
Normal file
1641
P4. Matrix Factorization.ipynb
Normal file
File diff suppressed because one or more lines are too long
1391
P5. Graph-based.ipynb
Normal file
1391
P5. Graph-based.ipynb
Normal file
File diff suppressed because one or more lines are too long
214
evaluation_measures.py
Normal file
214
evaluation_measures.py
Normal file
@ -0,0 +1,214 @@
|
|||||||
|
import os
|
||||||
|
import sys
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import math
|
||||||
|
from sklearn.preprocessing import normalize
|
||||||
|
from tqdm import tqdm
|
||||||
|
from datetime import datetime, date
|
||||||
|
import random
|
||||||
|
import scipy.sparse as sparse
|
||||||
|
from os import listdir
|
||||||
|
from os.path import isfile, join
|
||||||
|
from collections import defaultdict
|
||||||
|
|
||||||
|
|
||||||
|
def evaluate(test,
|
||||||
|
estimations_df,
|
||||||
|
reco,
|
||||||
|
super_reactions=[4,5],
|
||||||
|
topK=10):
|
||||||
|
|
||||||
|
estimations_df=estimations_df.copy()
|
||||||
|
reco=reco.copy()
|
||||||
|
test_df=test.copy()
|
||||||
|
|
||||||
|
# prepare testset
|
||||||
|
test_df.columns=['user', 'item', 'rating', 'timestamp']
|
||||||
|
test_df['user_code'] = test_df['user'].astype("category").cat.codes
|
||||||
|
test_df['item_code'] = test_df['item'].astype("category").cat.codes
|
||||||
|
|
||||||
|
user_code_id = dict(enumerate(test_df['user'].astype("category").cat.categories))
|
||||||
|
user_id_code = dict((v, k) for k, v in user_code_id.items())
|
||||||
|
item_code_id = dict(enumerate(test_df['item'].astype("category").cat.categories))
|
||||||
|
item_id_code = dict((v, k) for k, v in item_code_id.items())
|
||||||
|
|
||||||
|
test_ui = sparse.csr_matrix((test_df['rating'], (test_df['user_code'], test_df['item_code'])))
|
||||||
|
|
||||||
|
#prepare estimations
|
||||||
|
estimations_df.columns=['user', 'item' ,'score']
|
||||||
|
estimations_df['user_code']=[user_id_code[user] for user in estimations_df['user']]
|
||||||
|
estimations_df['item_code']=[item_id_code[item] for item in estimations_df['item']]
|
||||||
|
estimations=sparse.csr_matrix((estimations_df['score'], (estimations_df['user_code'], estimations_df['item_code'])), shape=test_ui.shape)
|
||||||
|
|
||||||
|
#compute_estimations
|
||||||
|
estimations_df=estimations_metrics(test_ui, estimations)
|
||||||
|
|
||||||
|
#prepare reco
|
||||||
|
users=reco[:,:1]
|
||||||
|
items=reco[:,1::2]
|
||||||
|
# Let's use inner ids instead of real ones
|
||||||
|
users=np.vectorize(lambda x: user_id_code.setdefault(x, -1))(users) # maybe users we recommend are not in test set
|
||||||
|
items=np.vectorize(lambda x: item_id_code.setdefault(x, -1))(items) # maybe items we recommend are not in test set
|
||||||
|
# Let's put them into one array
|
||||||
|
reco=np.concatenate((users, items), axis=1)
|
||||||
|
|
||||||
|
#compute ranking metrics
|
||||||
|
ranking_df=ranking_metrics(test_ui, reco, super_reactions=super_reactions, topK=topK)
|
||||||
|
|
||||||
|
#compute diversity metrics
|
||||||
|
diversity_df=diversity_metrics(test_ui, reco, topK)
|
||||||
|
|
||||||
|
result=pd.concat([estimations_df, ranking_df, diversity_df], axis=1)
|
||||||
|
|
||||||
|
return(result)
|
||||||
|
|
||||||
|
|
||||||
|
def ranking_metrics(test_ui, reco, super_reactions=[], topK=10):
|
||||||
|
|
||||||
|
nb_items=test_ui.shape[1]
|
||||||
|
relevant_users, super_relevant_users, prec, rec, F_1, F_05, prec_super, rec_super, ndcg, mAP, MRR, LAUC, HR=\
|
||||||
|
0,0,0,0,0,0,0,0,0,0,0,0,0
|
||||||
|
|
||||||
|
cg = (1.0 / np.log2(np.arange(2, topK + 2)))
|
||||||
|
cg_sum = np.cumsum(cg)
|
||||||
|
|
||||||
|
for (nb_user, user) in tqdm(enumerate(reco[:,0])):
|
||||||
|
u_rated_items=test_ui.indices[test_ui.indptr[user]:test_ui.indptr[user+1]]
|
||||||
|
nb_u_rated_items=len(u_rated_items)
|
||||||
|
if nb_u_rated_items>0: # skip users with no items in test set (still possible that there will be no super items)
|
||||||
|
relevant_users+=1
|
||||||
|
|
||||||
|
u_super_items=u_rated_items[np.vectorize(lambda x: x in super_reactions)\
|
||||||
|
(test_ui.data[test_ui.indptr[user]:test_ui.indptr[user+1]])]
|
||||||
|
# more natural seems u_super_items=[item for item in u_rated_items if test_ui[user,item] in super_reactions]
|
||||||
|
# but accesing test_ui[user,item] is expensive -we should avoid doing it
|
||||||
|
if len(u_super_items)>0:
|
||||||
|
super_relevant_users+=1
|
||||||
|
|
||||||
|
user_successes=np.zeros(topK)
|
||||||
|
nb_user_successes=0
|
||||||
|
user_super_successes=np.zeros(topK)
|
||||||
|
nb_user_super_successes=0
|
||||||
|
|
||||||
|
# evaluation
|
||||||
|
for (item_position,item) in enumerate(reco[nb_user,1:topK+1]):
|
||||||
|
if item in u_rated_items:
|
||||||
|
user_successes[item_position]=1
|
||||||
|
nb_user_successes+=1
|
||||||
|
if item in u_super_items:
|
||||||
|
user_super_successes[item_position]=1
|
||||||
|
nb_user_super_successes+=1
|
||||||
|
|
||||||
|
prec_u=nb_user_successes/topK
|
||||||
|
prec+=prec_u
|
||||||
|
|
||||||
|
rec_u=nb_user_successes/nb_u_rated_items
|
||||||
|
rec+=rec_u
|
||||||
|
|
||||||
|
F_1+=2*(prec_u*rec_u)/(prec_u+rec_u) if prec_u+rec_u>0 else 0
|
||||||
|
F_05+=(0.5**2+1)*(prec_u*rec_u)/(0.5**2*prec_u+rec_u) if prec_u+rec_u>0 else 0
|
||||||
|
|
||||||
|
prec_super+=nb_user_super_successes/topK
|
||||||
|
rec_super+=nb_user_super_successes/max(len(u_super_items),1)
|
||||||
|
ndcg+=np.dot(user_successes,cg)/cg_sum[min(topK, nb_u_rated_items)-1]
|
||||||
|
|
||||||
|
cumsum_successes=np.cumsum(user_successes)
|
||||||
|
mAP+=np.dot(cumsum_successes/np.arange(1,topK+1), user_successes)/min(topK, nb_u_rated_items)
|
||||||
|
MRR+=1/(user_successes.nonzero()[0][0]+1) if user_successes.nonzero()[0].size>0 else 0
|
||||||
|
LAUC+=(np.dot(cumsum_successes, 1-user_successes)+\
|
||||||
|
(nb_user_successes+nb_u_rated_items)/2*((nb_items-nb_u_rated_items)-(topK-nb_user_successes)))/\
|
||||||
|
((nb_items-nb_u_rated_items)*nb_u_rated_items)
|
||||||
|
|
||||||
|
HR+=nb_user_successes>0
|
||||||
|
|
||||||
|
|
||||||
|
result=[]
|
||||||
|
result.append(('precision', prec/relevant_users))
|
||||||
|
result.append(('recall', rec/relevant_users))
|
||||||
|
result.append(('F_1', F_1/relevant_users))
|
||||||
|
result.append(('F_05', F_05/relevant_users))
|
||||||
|
result.append(('precision_super', prec_super/super_relevant_users))
|
||||||
|
result.append(('recall_super', rec_super/super_relevant_users))
|
||||||
|
result.append(('NDCG', ndcg/relevant_users))
|
||||||
|
result.append(('mAP', mAP/relevant_users))
|
||||||
|
result.append(('MRR', MRR/relevant_users))
|
||||||
|
result.append(('LAUC', LAUC/relevant_users))
|
||||||
|
result.append(('HR', HR/relevant_users))
|
||||||
|
|
||||||
|
df_result=pd.DataFrame()
|
||||||
|
if len(result)>0:
|
||||||
|
df_result=(pd.DataFrame(list(zip(*result))[1])).T
|
||||||
|
df_result.columns=list(zip(*result))[0]
|
||||||
|
return df_result
|
||||||
|
|
||||||
|
|
||||||
|
def estimations_metrics(test_ui, estimations):
|
||||||
|
result=[]
|
||||||
|
|
||||||
|
RMSE=(np.sum((estimations.data-test_ui.data)**2)/estimations.nnz)**(1/2)
|
||||||
|
result.append(['RMSE', RMSE])
|
||||||
|
|
||||||
|
MAE=np.sum(abs(estimations.data-test_ui.data))/estimations.nnz
|
||||||
|
result.append(['MAE', MAE])
|
||||||
|
|
||||||
|
df_result=pd.DataFrame()
|
||||||
|
if len(result)>0:
|
||||||
|
df_result=(pd.DataFrame(list(zip(*result))[1])).T
|
||||||
|
df_result.columns=list(zip(*result))[0]
|
||||||
|
return df_result
|
||||||
|
|
||||||
|
def diversity_metrics(test_ui, reco, topK=10):
|
||||||
|
|
||||||
|
frequencies=defaultdict(int)
|
||||||
|
|
||||||
|
for item in list(set(test_ui.indices)):
|
||||||
|
frequencies[item]=0
|
||||||
|
|
||||||
|
for item in reco[:,1:].flat:
|
||||||
|
frequencies[item]+=1
|
||||||
|
|
||||||
|
nb_reco_outside_test=frequencies[-1]
|
||||||
|
del frequencies[-1]
|
||||||
|
|
||||||
|
frequencies=np.array(list(frequencies.values()))
|
||||||
|
|
||||||
|
nb_rec_items=len(frequencies[frequencies>0])
|
||||||
|
nb_reco_inside_test=np.sum(frequencies)
|
||||||
|
|
||||||
|
frequencies=frequencies/np.sum(frequencies)
|
||||||
|
frequencies=np.sort(frequencies)
|
||||||
|
|
||||||
|
with np.errstate(divide='ignore'): # let's put zeros we items with 0 frequency and ignore division warning
|
||||||
|
log_frequencies=np.nan_to_num(np.log(frequencies), posinf=0, neginf=0)
|
||||||
|
|
||||||
|
result=[]
|
||||||
|
result.append(('Reco in test', nb_reco_inside_test/(nb_reco_inside_test+nb_reco_outside_test)))
|
||||||
|
result.append(('Test coverage', nb_rec_items/test_ui.shape[1]))
|
||||||
|
result.append(('Shannon', -np.dot(frequencies, log_frequencies)))
|
||||||
|
result.append(('Gini', np.dot(frequencies, np.arange(1-len(frequencies), len(frequencies), 2))/(len(frequencies)-1)))
|
||||||
|
|
||||||
|
df_result=(pd.DataFrame(list(zip(*result))[1])).T
|
||||||
|
df_result.columns=list(zip(*result))[0]
|
||||||
|
return df_result
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def evaluate_all(test,
|
||||||
|
dir_path="Recommendations generated/ml-100k/",
|
||||||
|
super_reactions=[4,5],
|
||||||
|
topK=10):
|
||||||
|
|
||||||
|
models = list(set(['_'.join(f.split('_')[:2]) for f in listdir(dir_path)
|
||||||
|
if isfile(dir_path+f)]))
|
||||||
|
result=[]
|
||||||
|
for model in models:
|
||||||
|
estimations_df=pd.read_csv('{}{}_estimations.csv'.format(dir_path, model), header=None)
|
||||||
|
reco=np.loadtxt('{}{}_reco.csv'.format(dir_path, model), delimiter=',')
|
||||||
|
to_append=evaluate(test, estimations_df, reco, super_reactions, topK)
|
||||||
|
|
||||||
|
to_append.insert(0, "Model", model)
|
||||||
|
result.append(to_append)
|
||||||
|
result=pd.concat(result)
|
||||||
|
result=result.sort_values(by='recall', ascending=False)
|
||||||
|
return result
|
||||||
75
helpers.py
Normal file
75
helpers.py
Normal file
@ -0,0 +1,75 @@
|
|||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
import scipy.sparse as sparse
|
||||||
|
import surprise as sp
|
||||||
|
import time
|
||||||
|
from collections import defaultdict
|
||||||
|
from itertools import chain
|
||||||
|
|
||||||
|
def data_to_csr(train_read, test_read):
|
||||||
|
train_read.columns=['user', 'item', 'rating', 'timestamp']
|
||||||
|
test_read.columns=['user', 'item', 'rating', 'timestamp']
|
||||||
|
|
||||||
|
# Let's build whole dataset
|
||||||
|
train_and_test=pd.concat([train_read, test_read], axis=0, ignore_index=True)
|
||||||
|
train_and_test['user_code'] = train_and_test['user'].astype("category").cat.codes
|
||||||
|
train_and_test['item_code'] = train_and_test['item'].astype("category").cat.codes
|
||||||
|
|
||||||
|
user_code_id = dict(enumerate(train_and_test['user'].astype("category").cat.categories))
|
||||||
|
user_id_code = dict((v, k) for k, v in user_code_id.items())
|
||||||
|
item_code_id = dict(enumerate(train_and_test['item'].astype("category").cat.categories))
|
||||||
|
item_id_code = dict((v, k) for k, v in item_code_id.items())
|
||||||
|
|
||||||
|
train_df=pd.merge(train_read, train_and_test, on=list(train_read.columns))
|
||||||
|
test_df=pd.merge(test_read, train_and_test, on=list(train_read.columns))
|
||||||
|
|
||||||
|
# Take number of users and items
|
||||||
|
(U,I)=(train_and_test['user_code'].max()+1, train_and_test['item_code'].max()+1)
|
||||||
|
|
||||||
|
# Create sparse csr matrices
|
||||||
|
train_ui = sparse.csr_matrix((train_df['rating'], (train_df['user_code'], train_df['item_code'])), shape=(U, I))
|
||||||
|
test_ui = sparse.csr_matrix((test_df['rating'], (test_df['user_code'], test_df['item_code'])), shape=(U, I))
|
||||||
|
|
||||||
|
return train_ui, test_ui, user_code_id, user_id_code, item_code_id, item_id_code
|
||||||
|
|
||||||
|
|
||||||
|
def get_top_n(predictions, n=10):
|
||||||
|
|
||||||
|
# Here we create a dictionary which items are lists of pairs (item, score)
|
||||||
|
top_n = defaultdict(list)
|
||||||
|
for uid, iid, true_r, est, _ in predictions:
|
||||||
|
top_n[uid].append((iid, est))
|
||||||
|
|
||||||
|
result=[]
|
||||||
|
# Let's choose k best items in the format: (user, item1, score1, item2, score2, ...)
|
||||||
|
for uid, user_ratings in top_n.items():
|
||||||
|
user_ratings.sort(key=lambda x: x[1], reverse=True)
|
||||||
|
result.append([uid]+list(chain(*user_ratings[:n])))
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
def ready_made(algo, reco_path, estimations_path):
|
||||||
|
reader = sp.Reader(line_format='user item rating timestamp', sep='\t')
|
||||||
|
trainset = sp.Dataset.load_from_file('./Datasets/ml-100k/train.csv', reader=reader)
|
||||||
|
trainset = trainset.build_full_trainset() # <class 'surprise.trainset.Trainset'> -> it is needed for using Surprise package
|
||||||
|
|
||||||
|
testset = sp.Dataset.load_from_file('./Datasets/ml-100k/test.csv', reader=reader)
|
||||||
|
testset = sp.Trainset.build_testset(testset.build_full_trainset())
|
||||||
|
|
||||||
|
algo.fit(trainset)
|
||||||
|
|
||||||
|
antitrainset = trainset.build_anti_testset() # We want to predict ratings of pairs (user, item) which are not in train set
|
||||||
|
print('Generating predictions...')
|
||||||
|
predictions = algo.test(antitrainset)
|
||||||
|
print('Generating top N recommendations...')
|
||||||
|
top_n = get_top_n(predictions, n=10)
|
||||||
|
top_n=pd.DataFrame(top_n)
|
||||||
|
top_n.to_csv(reco_path, index=False, header=False)
|
||||||
|
|
||||||
|
print('Generating predictions...')
|
||||||
|
predictions = algo.test(testset)
|
||||||
|
predictions_df=[]
|
||||||
|
for uid, iid, true_r, est, _ in predictions:
|
||||||
|
predictions_df.append([uid, iid, est])
|
||||||
|
predictions_df=pd.DataFrame(predictions_df)
|
||||||
|
predictions_df.to_csv(estimations_path, index=False, header=False)
|
||||||
Loading…
Reference in New Issue
Block a user