pytorch-CFGAN

README.md

@@ -1,2 +1,14 @@
 # CFGAN
-Implementation of CFGAN
+Implementation of CFGAN. The implementation based on Pytorch is done, and the max precision on ML100k is 0.49 in 1000 train epochs.
+
+Next, we consider implementing another version based on Tensorflow.
+
+Author:
+    Xuxin Zhang, HUST, [email protected]
+
+Reference:
+Chae D K , Kang J S , Kim S W , et al. CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks[C]// the 27th ACM International Conference. ACM, 2018.
+
+Baseline:
+https://github.com/1051003502/CFGAN
+

cfgan.py

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+# -*- coding: utf-8 -*-
+"""
+Author:
+    Xuxin Zhang,[email protected]
+Reference: Chae D K , Kang J S , Kim S W , et al.
+CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks[C]// the 27th ACM International Conference. ACM, 2018.
+
+"""
+import torch
+import torch.nn as nn
+
+class discriminator(nn.Module):
+    def __init__(self,itemCount,info_shape):
+        super(discriminator,self).__init__()
+        self.dis=nn.Sequential(
+            nn.Linear(itemCount+info_shape,1024),
+            nn.ReLU(True),
+            nn.Linear(1024,128),
+            nn.ReLU(True),
+            nn.Linear(128,16),
+            nn.ReLU(True),
+            nn.Linear(16,1),
+            nn.Sigmoid()
+        )
+    def forward(self,data,condition):
+        data_c  = torch.cat((data,condition),1)
+        result=self.dis( data_c )
+        return result  
+
+class generator(nn.Module):
+    def __init__(self,itemCount,info_shape):
+        self.itemCount = itemCount
+        super(generator,self).__init__()
+        self.gen=nn.Sequential(
+            nn.Linear(self.itemCount+info_shape, 256),
+            nn.ReLU(True),
+            nn.Linear(256, 512),
+            nn.ReLU(True),
+            nn.Linear(512,1024),
+            nn.ReLU(True),
+            nn.Linear(1024, itemCount),
+            nn.Sigmoid()
+        )
+    def forward(self,noise,useInfo):
+        G_input = torch.cat([noise, useInfo], 1)
+        result=self.gen(G_input)
+        return result   

data.py

@@ -0,0 +1,106 @@
+# -*- coding: utf-8 -*-
+"""
+Author:
+    Xuxin Zhang,[email protected]
+Reference: Chae D K , Kang J S , Kim S W , et al.
+CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks[C]// the 27th ACM International Conference. ACM, 2018.
+
+"""
+
+from collections import defaultdict
+import torch
+import pandas as pd
+
+
+def loadUseInfo(trainFile,splitMark):
+    print(trainFile)
+    UseInfo = pd.DataFrame(columns=["userId","useAge","useGender","useOccupation","useZipcode"])
+    index = 0
+    for line in open(trainFile):
+        userId, useAge, useGender, useOccupation, useZipcode = line.strip().split(splitMark)
+        UseInfo.loc['%d'%index]=[userId, useAge, useGender, useOccupation, useZipcode]
+        index = index + 1
+    UseInfo.to_csv("data/ml-100k/userInfo.csv",index=False)
+    print("Demographic information about the users loading done: %d users" % (index))
+    return UseInfo 
+
+def loadItemInfo(trainFile,splitMark):
+#    trainFile = "data/ml-100k/u.item"
+#    splitMark =  "|"
+    print(trainFile)
+    ItemInfo = pd.DataFrame(columns=["movie_id","movie_title","release_date","video_release_date",
+                                     "IMDb_URL","unknowngenres","Action","Adventure",
+                                     "Animation","Childrens","Comedy","Crime",
+                                     "Documentary","Drama","Fantasy","Film_Noir",
+                                     "Horror","Musical","Mystery","Romance",
+                                     "Sci_Fi","Thriller","War","Western"])
+    index = 0
+    for line in open(trainFile,encoding = 'ISO-8859-1'):
+        ItemInfo.loc['%d'%index]=line.strip().split(splitMark)
+        index = index + 1
+    ItemInfo.to_csv("data/ml-100k/itemInfo.csv")
+    print("Information about the items loading done: %d users" % (index))
+    return ItemInfo  
+
+
+def loadTrainingData(trainFile,splitMark):
+    trainSet = defaultdict(list) 
+    max_u_id = -1
+    max_i_id = -1
+    for line in open(trainFile):
+        userId, itemId, rating,_ = line.strip().split(splitMark)
+        userId = int(userId)
+        itemId = int(itemId)
+        trainSet[userId].append(itemId)
+        max_u_id = max(userId, max_u_id)
+        max_i_id = max(itemId, max_i_id)
+    userCount = max_u_id + 1
+    itemCount = max_i_id + 1
+    print("Training data loading done" )
+    return trainSet,userCount,itemCount  
+
+
+def loadTestData(testFile,splitMark):
+    testSet = defaultdict(list)
+    max_u_id = -1
+    max_i_id = -1
+    for line in open(testFile):
+        userId, itemId, rating,_ = line.strip().split(splitMark)
+        userId = int(userId)
+        itemId = int(itemId)
+        testSet[userId].append(itemId)
+        max_u_id = max(userId, max_u_id)
+        max_i_id = max(itemId, max_i_id)
+    userCount = max_u_id + 1
+    itemCount = max_i_id + 1
+    print("Test data loading done")
+    return testSet,userCount,itemCount
+
+
+def to_Vectors(trainSet, userCount, itemCount, userList_test, mode):
+
+    testMaskDict = defaultdict(lambda: [0] * itemCount)
+    batchCount = userCount #改动  直接写成userCount
+    if mode == "itemBased":#改动  itemCount userCount互换   batchCount是物品数
+        userCount = itemCount
+        itemCount = batchCount
+        batchCount = userCount
+    trainDict = defaultdict(lambda: [0] * itemCount)
+    for userId, i_list in trainSet.items():
+        for itemId in i_list:
+            testMaskDict[userId][itemId] = -99999
+            if mode == "userBased":
+                trainDict[userId][itemId] = 1.0
+            else:
+                trainDict[itemId][userId] = 1.0
+
+    trainVector = []
+    for batchId in range(batchCount):
+        trainVector.append(trainDict[batchId])
+
+    testMaskVector = []
+    for userId in userList_test:
+        testMaskVector.append(testMaskDict[userId])
+#    print("Converting to vectors done....")
+    return (torch.Tensor(trainVector)), torch.Tensor(testMaskVector), batchCount
+

data/ml-100k/README

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+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
+<[email protected]>. 
+
+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.

data/ml-100k/allbut.pl

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+#!/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;
+	}
+}