rgg

app.py

@@ -0,0 +1,109 @@
+import pandas as pd
+import plotly.express as px
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+from math import sqrt
+import matplotlib
+
+matplotlib.use("Agg")
+
+
+genre_df = pd.read_csv(r"ml-100k\ml-100k\u.genre",
+                       sep="|", names=["genreName", "count"])
+
+
+user_df = pd.read_csv(r"ml-100k\ml-100k\u.user", sep="|",
+                      names=["userID", "age", "gender", "occupation", "zip_code"])
+movie_df = pd.read_csv(r"ml-100k\ml-100k\u.item", sep="|", names=["itemID", "title", "release_date", "video_release_date", "IMDb_URL", "unknown", "Action", "Adventure", "Animation", "Children's", "Comedy", "Crime", "Documentary", "Drama", "Fantasy", "Film-Noir", "Horror", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western"],encoding='latin-1')
+
+prediction_df = pd.read_csv(r"pred.csv", sep=",")
+
+
+def mappingMovie(mid):
+    return movie_df.loc[movie_df["itemID"].values==mid]["title"].values[0]
+
+prediction_df["Movie Title"] = prediction_df["itemID"].apply(mappingMovie)
+
+
+df = pd.read_csv(r"ml-100k\ml-100k\u.data", sep="\t", names=["userID", "itemID", "rating", "timestamp"])    
+    
+
+rating_df = df[df["rating"]>=1.0]
+
+rating_df["Movie Title"] = rating_df["itemID"].apply(mappingMovie)
+
+print(rating_df.head(7))
+
+num_users = len(prediction_df["userID"].unique())
+
+def get_top_rated_movies_from_user(id):
+    entire_df= rating_df[rating_df["userID"]==id].sort_values(by="rating", ascending=False).head(10)
+    return entire_df
+def get_recommendations(id):
+    entire_df= prediction_df[prediction_df["userID"]==id].sort_values(by="prediction", ascending=False).head(10)
+    entire_df.drop(columns=["Unnamed: 0"], inplace=True)
+    return entire_df
+
+def update_user(id):
+    return get_top_rated_movies_from_user(id), get_recommendations(id)
+def random_user():
+    return update_user(np.random.randint(0, num_users-1))
+
+demo = gr.Blocks()
+
+with demo:
+  gr.Markdown("""
+  <div>
+  <h1 style='text-align: center'>Movie Recommender</h1>
+  Collaborative Filtering is used to predict the top 10 recommended movies for a particular user from the dataset based on that user and previous movies they have rated.
+  
+  Note: Currently there is a bug with sliders. If you "click and drag" on the slider it will not use the correct user. Please only "click" on the slider :D.
+  </div>
+  """)
+    
+  with gr.Box():
+    gr.Markdown(
+    """
+    ### Input
+    #### Select a user to get recommendations for.
+    """)
+
+    inp1 = gr.Slider(0, num_users-1, value=0, label='User')
+    # btn1 = gr.Button('Random User')
+
+    # top_rated_from_user = get_top_rated_from_user(0)
+    gr.Markdown(
+    """
+    <br>
+    """)
+    gr.Markdown(
+    """
+    #### Movies with the Highest Ratings from this user
+    """)
+    df1 = gr.DataFrame(headers=["title", "genres"], datatype=["str", "str"], interactive=False)
+
+  with gr.Box():
+    # recommendations = get_recommendations(0)
+    gr.Markdown(
+    """
+    ### Output
+    #### Top 10 movie recommendations
+    """)
+    df2 = gr.DataFrame(headers=["title", "genres"], datatype=["str", "str"], interactive=False)
+
+  gr.Markdown("""
+  <p style='text-align: center'>
+      <a href='https://keras.io/examples/structured_data/collaborative_filtering_movielens/' target='_blank' style='text-decoration: underline'></a>
+      <br>
+      Space by Scott Krstyen (mindwrapped)
+      </p>
+  """)
+  
+  
+  inp1.change(fn=update_user,
+              inputs=inp1,
+              outputs=[df1, df2])
+  
+
+demo.launch(debug=True)

ml-100k/ml-100k/README

@@ -0,0 +1,157 @@
+SUMMARY & USAGE LICENSE
+=============================================
+
+MovieLens data sets were collected by the GroupLens Research Project
+at the University of Minnesota.
+ 
+This data set consists of:
+	* 100,000 ratings (1-5) from 943 users on 1682 movies. 
+	* Each user has rated at least 20 movies. 
+        * Simple demographic info for the users (age, gender, occupation, zip)
+
+The data was collected through the MovieLens web site
+(movielens.umn.edu) during the seven-month period from September 19th, 
+1997 through April 22nd, 1998. This data has been cleaned up - users
+who had less than 20 ratings or did not have complete demographic
+information were removed from this data set. Detailed descriptions of
+the data file can be found at the end of this file.
+
+Neither the University of Minnesota nor any of the researchers
+involved can guarantee the correctness of the data, its suitability
+for any particular purpose, or the validity of results based on the
+use of the data set.  The data set may be used for any research
+purposes under the following conditions:
+
+     * The user may not state or imply any endorsement from the
+       University of Minnesota or the GroupLens Research Group.
+
+     * The user must acknowledge the use of the data set in
+       publications resulting from the use of the data set
+       (see below for citation information).
+
+     * The user may not redistribute the data without separate
+       permission.
+
+     * The user may not use this information for any commercial or
+       revenue-bearing purposes without first obtaining permission
+       from a faculty member of the GroupLens Research Project at the
+       University of Minnesota.
+
+If you have any further questions or comments, please contact GroupLens
+<grouplens-info@cs.umn.edu>. 
+
+CITATION
+==============================================
+
+To acknowledge use of the dataset in publications, please cite the 
+following paper:
+
+F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets:
+History and Context. ACM Transactions on Interactive Intelligent
+Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages.
+DOI=http://dx.doi.org/10.1145/2827872
+
+
+ACKNOWLEDGEMENTS
+==============================================
+
+Thanks to Al Borchers for cleaning up this data and writing the
+accompanying scripts.
+
+PUBLISHED WORK THAT HAS USED THIS DATASET
+==============================================
+
+Herlocker, J., Konstan, J., Borchers, A., Riedl, J.. An Algorithmic
+Framework for Performing Collaborative Filtering. Proceedings of the
+1999 Conference on Research and Development in Information
+Retrieval. Aug. 1999.
+
+FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
+==============================================
+
+The GroupLens Research Project is a research group in the Department
+of Computer Science and Engineering at the University of Minnesota.
+Members of the GroupLens Research Project are involved in many
+research projects related to the fields of information filtering,
+collaborative filtering, and recommender systems. The project is lead
+by professors John Riedl and Joseph Konstan. The project began to
+explore automated collaborative filtering in 1992, but is most well
+known for its world wide trial of an automated collaborative filtering
+system for Usenet news in 1996.  The technology developed in the
+Usenet trial formed the base for the formation of Net Perceptions,
+Inc., which was founded by members of GroupLens Research. Since then
+the project has expanded its scope to research overall information
+filtering solutions, integrating in content-based methods as well as
+improving current collaborative filtering technology.
+
+Further information on the GroupLens Research project, including
+research publications, can be found at the following web site:
+        
+        http://www.grouplens.org/
+
+GroupLens Research currently operates a movie recommender based on
+collaborative filtering:
+
+        http://www.movielens.org/
+
+DETAILED DESCRIPTIONS OF DATA FILES
+==============================================
+
+Here are brief descriptions of the data.
+
+ml-data.tar.gz   -- Compressed tar file.  To rebuild the u data files do this:
+                gunzip ml-data.tar.gz
+                tar xvf ml-data.tar
+                mku.sh
+
+u.data     -- The full u data set, 100000 ratings by 943 users on 1682 items.
+              Each user has rated at least 20 movies.  Users and items are
+              numbered consecutively from 1.  The data is randomly
+              ordered. This is a tab separated list of 
+	         user id | item id | rating | timestamp. 
+              The time stamps are unix seconds since 1/1/1970 UTC   
+
+u.info     -- The number of users, items, and ratings in the u data set.
+
+u.item     -- Information about the items (movies); this is a tab separated
+              list of
+              movie id | movie title | release date | video release date |
+              IMDb URL | unknown | Action | Adventure | Animation |
+              Children's | Comedy | Crime | Documentary | Drama | Fantasy |
+              Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi |
+              Thriller | War | Western |
+              The last 19 fields are the genres, a 1 indicates the movie
+              is of that genre, a 0 indicates it is not; movies can be in
+              several genres at once.
+              The movie ids are the ones used in the u.data data set.
+
+u.genre    -- A list of the genres.
+
+u.user     -- Demographic information about the users; this is a tab
+              separated list of
+              user id | age | gender | occupation | zip code
+              The user ids are the ones used in the u.data data set.
+
+u.occupation -- A list of the occupations.
+
+u1.base    -- The data sets u1.base and u1.test through u5.base and u5.test
+u1.test       are 80%/20% splits of the u data into training and test data.
+u2.base       Each of u1, ..., u5 have disjoint test sets; this if for
+u2.test       5 fold cross validation (where you repeat your experiment
+u3.base       with each training and test set and average the results).
+u3.test       These data sets can be generated from u.data by mku.sh.
+u4.base
+u4.test
+u5.base
+u5.test
+
+ua.base    -- The data sets ua.base, ua.test, ub.base, and ub.test
+ua.test       split the u data into a training set and a test set with
+ub.base       exactly 10 ratings per user in the test set.  The sets
+ub.test       ua.test and ub.test are disjoint.  These data sets can
+              be generated from u.data by mku.sh.
+
+allbut.pl  -- The script that generates training and test sets where
+              all but n of a users ratings are in the training data.
+
+mku.sh     -- A shell script to generate all the u data sets from u.data.

ml-100k/ml-100k/allbut.pl

@@ -0,0 +1,34 @@
+#!/usr/local/bin/perl
+
+# get args
+if (@ARGV < 3) {
+	print STDERR "Usage: $0 base_name start stop max_test [ratings ...]\n";
+	exit 1;
+}
+$basename = shift;
+$start = shift;
+$stop = shift;
+$maxtest = shift;
+
+# open files
+open( TESTFILE, ">$basename.test" ) or die "Cannot open $basename.test for writing\n";
+open( BASEFILE, ">$basename.base" ) or die "Cannot open $basename.base for writing\n";
+
+# init variables
+$testcnt = 0;
+
+while (<>) {
+	($user) = split;
+	if (! defined $ratingcnt{$user}) {
+		$ratingcnt{$user} = 0;
+	}
+	++$ratingcnt{$user};
+	if (($testcnt < $maxtest || $maxtest <= 0)
+	&& $ratingcnt{$user} >= $start && $ratingcnt{$user} <= $stop) {
+		++$testcnt;
+		print TESTFILE;
+	}
+	else {
+		print BASEFILE;
+	}
+}

ml-100k/ml-100k/mku.sh

@@ -0,0 +1,25 @@
+#!/bin/sh
+
+trap `rm -f tmp.$$; exit 1` 1 2 15
+
+for i in 1 2 3 4 5
+do
+	head -`expr $i \* 20000` u.data | tail -20000 > tmp.$$
+	sort -t"	" -k 1,1n -k 2,2n tmp.$$ > u$i.test
+	head -`expr \( $i - 1 \) \* 20000` u.data > tmp.$$
+	tail -`expr \( 5 - $i \) \* 20000` u.data >> tmp.$$
+	sort -t"	" -k 1,1n -k 2,2n tmp.$$ > u$i.base
+done
+
+allbut.pl ua 1 10 100000 u.data
+sort -t"	" -k 1,1n -k 2,2n ua.base > tmp.$$
+mv tmp.$$ ua.base
+sort -t"	" -k 1,1n -k 2,2n ua.test > tmp.$$
+mv tmp.$$ ua.test
+
+allbut.pl ub 11 20 100000 u.data
+sort -t"	" -k 1,1n -k 2,2n ub.base > tmp.$$
+mv tmp.$$ ub.base
+sort -t"	" -k 1,1n -k 2,2n ub.test > tmp.$$
+mv tmp.$$ ub.test
+

ml-100k/ml-100k/u.genre

@@ -0,0 +1,20 @@
+unknown|0
+Action|1
+Adventure|2
+Animation|3
+Children's|4
+Comedy|5
+Crime|6
+Documentary|7
+Drama|8
+Fantasy|9
+Film-Noir|10
+Horror|11
+Musical|12
+Mystery|13
+Romance|14
+Sci-Fi|15
+Thriller|16
+War|17
+Western|18
+

ml-100k/ml-100k/u.info

@@ -0,0 +1,3 @@
+943 users
+1682 items
+100000 ratings

ml-100k/ml-100k/u.occupation

@@ -0,0 +1,21 @@
+administrator
+artist
+doctor
+educator
+engineer
+entertainment
+executive
+healthcare
+homemaker
+lawyer
+librarian
+marketing
+none
+other
+programmer
+retired
+salesman
+scientist
+student
+technician
+writer

ml-100k/ml-100k/u.user

@@ -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