Libfm 1.42.manual
libfm-1.42.manual
libfm-1.42.manual
libfm-1.42.manual
libfm-1.42.manual
libfm-1.42.manual
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libFM 1.4.2 - Manual
Steffen Rendle
srendle@libfm.org
http://www.libfm.org/
September 14, 2014
Contents
1 Installation
1.1 Compiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
2
2 Data Format
2.1 Text Format . . . . . . . . . . . . . . .
2.1.1 Converting Recommender Files
2.2 Binary Format* . . . . . . . . . . . . .
2.2.1 Transpose data . . . . . . . . .
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3
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3 libFM
3.1 Mandatory Parameters . . . . . . . . . . . . .
3.2 Optional Parameters . . . . . . . . . . . . . .
3.2.1 Basic Parameters . . . . . . . . . . . .
3.2.2 Advanced Parameters* . . . . . . . . .
3.3 Learning Methods . . . . . . . . . . . . . . .
3.3.1 Stochastic Gradient Descent (SGD) .
3.3.2 Alternating Least Squares (ALS) . . .
3.3.3 Markov Chain Monte Carlo (MCMC)
3.3.4 Adaptive SGD (SGDA) . . . . . . . .
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5
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4 Block Structure (BS) Extension*
4.1 Data Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Running libFM with BS data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Notes about BS Usage in libFM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
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5 License
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Note: advanced chapters are marked with *.
1
1
Installation
• Linux: compile libFM by following the instructions in section 1.1.
• MacOS X: compile libFM by following the instructions in section 1.1.
• Windows: download the compiled executable from http://www.libfm.org/libfm-1.40.windows.
zip. You can skip section 1.1. Please note that the version of the compiled executable is libFM
1.4.0. This version has the same functionality as libFM 1.4.2 but it has a different license.
1.1
Compiling
libFM has been tested with the GNU compiler collection and GNU make. Both should be available in
Linux and MacOS X.
With the following steps, you can build libFM:
1. Download libFM source code: http://www.libfm.org/libfm-1.42.src.tar.gz
2. Unzip and untar: e.g. tar -xzvf libfm-1.42.src.tar.gz
3. Enter the directory libfm-1.42.src and compile the tools: make all
Overview of files
• license.txt: license for usage of libFM
• history.txt: version history and changes
• readme.pdf: this manual
• Makefile: compiles the executables using make
• bin: the folder with the executables1
– libFM: the libFM tool
– convert: a tool for converting text-files into binary format
– transpose: a tool for transposing binary design matrices
• scripts
– triple format to libfm.pl: a Perl script for converting comma/tab-separated datasets into
libFM-format.
• src: the source files of libFM and the tools
1 The
executables have to be built with make, see sec. 1.1.
2
2
Data Format
libFM supports two file formats for input data: a text format and a binary format. Working with the
text format is easier and recommended for new libFM-users.
2.1
Text Format
The data format is the same as in SVMlite [3] and LIBSVM [1]2 : Each row contains a training case
(x, y) for the real-valued feature vector x with the target y. The row states first the value y and then the
non-zero values of x. For binary classification, cases with y > 0 are regarded as the positive class and
with y ≤ 0 as the negative class.
Example
4 0:1.5 3:-7.9
2 1:1e-5 3:2
-1 6:1
...
This file contains three cases. The first column states the target of each of the three case: i.e. 4 for the
first case, 2 for the second and -1 for the third. After the target, each line contains the non-zero elements
of x, where an entry like 0:1.5 reads x0 = 1.5 and 3:-7.9 means x3 = −7.9, etc. That means the left
side of INDEX:VALUE states the index within x whereas the right side states the value of xINDEX , i.e.
xINDEX = VALUE.
In total the data from the example describes the following design matrix X and target vector y:
1.5
0.0 0.0 −7.9 0.0 0.0 0.0
4
2.0 0.0 0.0 0.0 , y = 2
X = 0.0 10−5 0.0
(1)
0.0
0.0 0.0
0.0 0.0 0.0 1.0
−1
2.1.1
Converting Recommender Files
In recommender systems often a file format like userid itemid rating is used. A Perl script to convert
such datasets (and also more complex ones like in context-aware settings) to the libFM file format is
included in the scripts directory. E.g. for converting the ratings.dat file from the Movielens 1M3
dataset into libFM format, call:
./triple_format_to_libfm.pl -in ratings.dat -target 2 -delete_column 3 -separator "::"
The output will be written to a file with extension .libfm. In the example the output is written to
ratings.dat.libfm.
If a single dataset consists of multiple files, e.g. a train and test split, then the convertion script
should be called with both files (several files for the -in option):
./triple_format_to_libfm.pl -in train.txt,test.txt -target 2 -separator "\t"
Warning: If you would run the convertion script for each file separately, the variables (ids) would not
match. E.g. the n-th variable in the first file is different from the n-th variable in the second file.
2.2
Binary Format*
Besides the standard text format, libFM supports a binary data format. The advantages of the binary
format are: (1) faster reading, (2) if your data does not fit into main memory, the binary data format
supports caching data on hard disc and keeping only a small portion in memory (use the --cache size
in libFM), (3) if you work with ALS and MCMC, you can precompute the transposed design matrix
which saves time when the data set is read.
To convert a file in libFM text format to the binary data format, use the tool convert in the bin
folder:
2 Note: libFM supports files where the variable index starts with 0. In SVMlite and LIBSVM, the index has to start
with 1. libFM supports both, i.e. files that work with SVMlite or LIBSVM can be used directly in libFM.
3 http://www.grouplens.org/node/12
3
Convert
Version: 1.4.2
Author: Steffen Rendle, srendle@libfm.org
WWW:
http://www.libfm.org/
This program comes with ABSOLUTELY NO WARRANTY; for details see license.txt.
This is free software, and you are welcome to redistribute it under certain
conditions; for details see license.txt.
----------------------------------------------------------------------------help
this screen
-ifile
input file name, file has to be in binary sparse format
[MANDATORY]
-ofilex
output file name for x [MANDATORY]
-ofiley
output file name for y [MANDATORY]
Example
To convert the Movielens dataset from the example above to binary format:
./convert --ifile ratings.dat.libfm --ofilex ratings.x --ofiley ratings.y
The output will consist of two files: (1) a file containing the design matrix X, i.e. the predictor variables
and (2) a file containing the prediction targets y. It is recommended that these files have the ending .x
for the design matrix and .y for the targets.
2.2.1
Transpose data
For MCMC and ALS learning, a transposed design matrix is used. If you use the text format, the data
is automatically transposed by libFM internally. However if you use binary format, the transposed data
has to be present in binary format as well. To transpose a design matrix to binary format use the tool
transpose.
---------------------------------------------------------------------------Transpose
Version: 1.4.2
Author: Steffen Rendle, srendle@libfm.org
WWW:
http://www.libfm.org/
This program comes with ABSOLUTELY NO WARRANTY; for details see license.txt.
This is free software, and you are welcome to redistribute it under certain
conditions; for details see license.txt.
----------------------------------------------------------------------------cache_size
cache size for data storage, default=200000000
-help
this screen
-ifile
input file name, file has to be in binary sparse format
[MANDATORY]
-ofile
output file name [MANDATORY]
Example
To transpose the Movielens dataset from the example above:
./transpose --ifile ratings.x --ofile ratings.xt
The output will be a transposed copy of the design matrix. It is recommended that the transposed file
has the ending .xt.
4
3
libFM
The libFM tool trains a Factorization Machine (FM) [4] model from training data -train and predicts
the test data -test. libFM has the following options:
---------------------------------------------------------------------------libFM
Version: 1.4.2
Author: Steffen Rendle, srendle@libfm.org
WWW:
http://www.libfm.org/
This program comes with ABSOLUTELY NO WARRANTY; for details see license.txt.
This is free software, and you are welcome to redistribute it under certain
conditions; for details see license.txt.
----------------------------------------------------------------------------cache_size
cache size for data storage (only applicable if data is
in binary format), default=infty
-dim
’k0,k1,k2’: k0=use bias, k1=use 1-way interactions,
k2=dim of 2-way interactions; default=1,1,8
-help
this screen
-init_stdev
stdev for initialization of 2-way factors; default=0.1
-iter
number of iterations; default=100
-learn_rate
learn_rate for SGD; default=0.1
-meta
filename for meta information about data set
-method
learning method (SGD, SGDA, ALS, MCMC); default=MCMC
-out
filename for output
-regular
’r0,r1,r2’ for SGD and ALS: r0=bias regularization,
r1=1-way regularization, r2=2-way regularization
-relation
BS: filenames for the relations, default=’’
-rlog
write measurements within iterations to a file;
default=’’
-task
r=regression, c=binary classification [MANDATORY]
-test
filename for test data [MANDATORY]
-train
filename for training data [MANDATORY]
-validation
filename for validation data (only for SGDA)
-verbosity
how much infos to print; default=0
3.1
Mandatory Parameters
• The first mandatory parameter to specify is the -task which is either classification (-task c) or
regression (-task r).
• Secondly training data (-train) and test data (-test) has to be present. You can use here a data
file in libFM-text format or binary format (see sec. 3.2.2 for details about using binary files).
• Third, the dimensionality of the factorization machine has to be specified with -dim. This argument
consists of three numbers: k0 , k1 , k2 .
– k0 ∈ {0, 1} determines if the global bias term w0 should be used in the model (see [4] for
details).
– k1 ∈ {0, 1} determines if one-way interactions (bias terms for each variable), i.e. w should be
used in the model (see [4] for details).
– k2 ∈ N0 gives the number of factors that are used for pairwise interactions, i.e. the k of
V ∈ Rp×k (see [4] for details).
Example An FM for a regression task using bias, 1-way interactions and a factorization of k = 8 for
pairwise interactions:
./libFM -task r -train ml1m-train.libfm -test ml1m-test.libfm -dim ’1,1,8’
5
3.2
3.2.1
Optional Parameters
Basic Parameters
• out: After training is finished, you can write all predictions of the test dataset to the file specified
by out. The out-file is in text format, has as many lines as the test dataset and the i-th line states
the prediction of the i-th test case. Note that for classification the output is the probability that
the case has the positive class.
• rlog: A logfile with statistics about each iteration is generated. The file is in CSV format using
TAB separated fields. Note that it depends on the learning method which fields are reported.
• verbosity: With the argument -verbosity 1, libFM prints more information. This is useful to
check if your data is read correctly, and to find errors.
3.2.2
Advanced Parameters*
Grouping You can group input variables using the meta option. Grouping can be used for MCMC,
SGDA and ALS to define a more complex regularization structure. Every group can have an individual
regularization parameter. To use grouping, the meta parameter expects the name of a textfile with as
many lines as there are input variables (columns of the design matrix). Each line specifies the group ID
of the corresponding input variable. Please note that IDs for groups should be numerical and start with
0. E.g. a grouping file for the design matrix of the example in eq. (1) (which has 7 columns; the largest
ID is 6) could be:
2
2
0
1
1
1
0
Which would read: in total there are three groups. The first two variables (columns in the design matrix)
have the same group, the third and last have the same group and the fourth to sixth variable have the
same group.
Binary Data and Caching In sec. 2.2 it was stated that the filenames for binary files should end with
.x for the design matrix, .y for the target and .xt for transposed data. If you want to use binary data in
libFM, the filenames for the command line arguments of training, test and validation should be specified
without the .x, .y, .xt ending. I.e. if you have compiled training (ml1m-train.x, ml1m-train.y,
ml1m-train.xt) and test data (ml1m-test.x, ml1m-test.y, ml1m-test.xt) call:
./libFM -task r -train ml1m-train -test ml1m-test -dim ’1,1,8’
libFM will automatically append the proper file extensions and load the data files that are necessary for
the learning algorithm.
If your data does not fit into memory, you can specify how much of a file libFM is allowed to keep
in memory:
./libFM -task r -train ml1m-train -test ml1m-test -dim ’1,1,8’ -cache_size 100000000
In this example, 100,000,000 Bytes (100 MB) would be used as cache for each .x or .xt file. Note that
the .y files are always read completely into memory.
If the argument cache size is not specified, all data is loaded into memory. Note: you should use
caching only if the data does not fit into memory because caching uses the harddisc which will be much
slower then memory access.
6
3.3
Learning Methods
By default MCMC inference is used for learning because MCMC is the most easiest to handle (no
learning rate, no regularization). In libFM you can choose from the following learning methods: SGD,
ALS, MCMC and SGDA. For all learning methods, the number of iterations iter has to be specified.
3.3.1
Stochastic Gradient Descent (SGD)
Use -method sgd for parameter learning with SGD [4]. For stochastic gradient descent the following
parameters have to be chosen:
• -learn rate: the learning rate aka step size of SGD which should have a non-zero or positive value.
• -regular: the regularization parameters which should have zero or positive value. For SGD you
can specify the regularization values the following way:
– One value (-regular value): all model parameters use the same regularization value.
– Three values (-regular ’value0,value1,value2’): 0-way interactions (w0 ) use value0 as
regularization, 1-way interactions (w) use value1 and pairwise ones (V ) use value2.
– No value: if the parameter -regular is not specified at all, this corresponds to no regularization, i.e. -regular 0.
• -init stdev: the standard deviation of the normal distribution that is used for initializing the
parameters V . You should use a non-zero, positive value here.
Please choose these arguments carefully as the prediction quality largely depends on good choices.
Example
./libFM -task r -train ml1m-train.libfm -test ml1m-test.libfm -dim ’1,1,8’ -iter 1000
-method sgd -learn_rate 0.01 -regular ’0,0,0.01’ -init_stdev 0.1
3.3.2
Alternating Least Squares (ALS)
Use -method als for parameter learning with ALS [8]. The following parameters have to be chosen:
• -regular: the regularization parameters which should have zero or positive value. For ALS you
can specify the regularization values the following way:
– One value (-regular value): all model parameters use the same regularization value.
– Three values (-regular ’value0,value1,value2’): 0-way interactions (w0 ) use value0 as
regularization, 1-way interactions (w) use value1 and pairwise ones (V ) use value2.
– Group specific values (-regular ’value0,value1g1,...,value1gm,value2g1,...,value2gm’),
i.e. for m groups there are 1 + 2 m many regularization values: if the input variables are
grouped, for each group and 1-way and 2-way interaction an individual regularization value
can be used.
– No value: if the parameter -regular is not specified at all, this corresponds to no regularization, i.e. -regular 0.
• -init stdev: the standard deviation of the normal distribution that is used for initializing the
parameters V . You should use a non-zero, positive value here.
Please choose these arguments carefully as the prediction quality largely depends on good choices.
Example
./libFM -task r -train ml1m-train.libfm -test ml1m-test.libfm -dim ’1,1,8’ -iter 1000
-method als -regular ’0,0,10’ -init_stdev 0.1
7
3.3.3
Markov Chain Monte Carlo (MCMC)
Use -method mcmc for parameter learning with MCMC [2]. The following parameters have to be chosen:
• -init stdev: the standard deviation of the normal distribution that is used for initializing the
parameters V . You should use a non-zero, positive value here.
Please choose this argument carefully as the speed of convergence depends on a good choice.
Example
./libFM -task r -train ml1m-train.libfm -test ml1m-test.libfm -dim ’1,1,8’ -iter 1000
-method mcmc -init_stdev 0.1
3.3.4
Adaptive SGD (SGDA)
Use -method sgda for parameter learning with adpative SGD [6]. With adaptive SGD, regularization
values (per group and layer) are found automatically. You have to specify a validation set that is used
to tune the regularization values:
• -validation: a data set containing training examples used as a validation set to tune the regularization values. This set should be distinct from the training dataset.
• -learn rate: the learning rate aka step size of SGD which should have a non-zero or positive value.
• -init stdev: the standard deviation of the normal distribution that is used for initializing the
parameters V . You should use a non-zero, positive value here.
Please choose these arguments carefully as the prediction quality largely depends on good choices.
Example
./libFM -task r -train ml1m-train.libfm -test ml1m-test.libfm -dim ’1,1,8’ -iter 1000
-method sgda -learn_rate 0.01 -init_stdev 0.1 -validation ml1m-val.libfm
8
4
Block Structure (BS) Extension*
(a) Training Data (Design Matrix)
X
1
1
1
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
1
1
1
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
1
1
0
0
1
0
0
0
0
1
0
0
1
12
15
2
37
5
5
1
1
1
0
0
0
0
0
0
0
1
1
1
1
30
30
30
25
25
28
28
.5
0
.5
.5
0
.5
.5
.5
.5
0
0
0
.5
0
0
.5
0
0
0
0
0
0
0
.5
.5
1
0
.5
0
0
0
0
0
.3
.3
0
0
0
0
0
.3
.3
.5
.5
.5
.5
.5
0
0
0
0
0
.5
.5
.3
.3
.5
.5
.5
0
0
0
0
.3
.3
.3
0
0
.5
.5
.3
.3
.3
0
0
0
0
.3
.3
.3
.5
.5
.5
.5
0
0
0
.5
.5
0
0
n
y
5
3
1
4
5
1
5
p
(b) Block Structure Representation of Design Matrix
ΦB1ΦB2ΦB3
0 0 0
0 1 1
0 2 2
1 2 3
1 3 4
2 0 5
2 2 5
n
y
5
3
1
4
5
1
5
XB1
1 0 0 1 0 30 0 0 .5 0 .5 .3 .3 .3 0
0 1 0 0 1 25 0 0 .5 .5 0 0 0 .5 .5
0 0 1 0 1 28 .3 .3 0 .3 0 .5 0 .5 0
pB1
X
B2
1
0
0
0
0
1
0
0
0
0
1
0
0
0
0
1
.5
0
.5
0
.5
.5
0
0
0
.5
0
0
0
0
.5
1
nB2
pB2
nB1
1
12
XB3 15
2
37
5
nB3
pB3
Figure 1: (a) LibFM data files (=representation of the design matrix X) might contain large blocks of
repeating patterns. (b) The BS extension of libFM allows to use a more compact representation of the
data files where repeating patterns are described just once. (figure adapted from [7])
In relational settings, design matrices might contain large blocks of repeating patterns (see Fig. 1a).
This can result in a very large design matrix which makes learning slow and uses lots of memory. The
BS extension of libFM allows to define and make use of the block structure in design matrices. Both
runtime and memory consumption will be linear in the BS data size instead of the original data size. For
details about large design matrices from relational data, see [7].
4.1
Data Format
The BS extension allows to define blocks (e.g. B1, B2, B3 in Fig. 1) and to use them in libFM. Each
block definition consists of:
• The design matrix (libFM-file) of the block (e.g. X B1 in Fig. 1).
• The mapping from train (or test) cases to the rows in the block (e.g. φB1 in Fig. 1).
• Optional grouping of variables in the design matrix (compare section 3.2.2).
For each block, the following files are expected:
9
Filename
.x
.xt
.train
.test
.groups
4.2
Description
The design matrix of the block in binary format (e.g. X B1 in Fig. 1).
The convert tool can be used to generate this from a file in libFM text
format (see section 3.2.2).
The transpose of .x in binary format. The transpose tool
can be used to generate this file from .x (see section 3.2.2).
The mapping from train rows to block rows (e.g. φB1 in Fig. 1). The
expected file is in text format with as many lines as the training dataset
(in the --train parameter). In each row, the file states the index4 of the
row in X B1 to which this training case links. E.g. if the entry in the 5th
row is 21, then the 5th train case uses the data in the 22nd row of X B1 .
Same as .train but now the lines in the test dataset are
linked to X B1 .
Optional file for grouping predictor variables. The same format as in
section 3.2.2 is expected.
Running libFM with BS data
The blocks are passed in the command line parameter --relation. Assuming two blocks (rel.user and
rel.item) have been defined, the call would be:
./libFM -task r -train ml1m-train -test ml1m-test -dim ’1,1,8’ --relation rel.user,rel.item
Note that for each block the files listed above have to be present (i.e. rel.user.x, rel.user.xt,
rel.user.train, rel.user.test, (rel.user.groups), rel.item.x, rel.item.xt, etc.).
4.3
Notes about BS Usage in libFM
• BS is only supported by MCMC and ALS/CD.
• Even when using BS, the --train and --test parameters are still mandatory and files have to be
specified here. The libFM files passed in --train and --test can have predictor variables as well
but might also be empty5 . The files can be in binary or text format.
• The namespaces of variable ids in BS design matrices are distinct. E.g. a variable with index 7 in
X B1 as well as in X B2 and the main train/test file X is considered to be different each. Internally,
libFM adds the offset of the largest variable id of the preceding block X Bi−1 to each id in block
X Bi . Thus it is recommended to start indexing all blocks with variable id 0 to avoid wasting
memory.
• The namespaces of the groups in BS files are distinct. Each group file can have groups starting
from 0 – overlaps are resolved the same way as with predictor ids.
• If no group files are passed, each block is automatically assumed to have a different group.
4 All
indices are 0-based. I.e. the index of the first row is 0, the index of the second row is 1, etc.
target should be still specified. The predictor variables can be empty.
5 The
10
5
License
Please see license.txt for details. If you use libFM in your work, please cite the following paper:
@article{rendle:tist2012,
author = {Rendle, Steffen},
title = {Factorization Machines with {libFM}},
journal = {ACM Trans. Intell. Syst. Technol.},
issue_date = {May 2012},
volume = {3},
number = {3},
month = May,
year = {2012},
issn = {2157-6904},
pages = {57:1--57:22},
articleno = {57},
numpages = {22},
publisher = {ACM},
address = {New York, NY, USA},
}
References
[1] Chih-Chung Chang and Chih-Jen Lin. Libsvm: A library for support vector machines. ACM Trans.
Intell. Syst. Technol., 2:27:1–27:27, May 2011.
[2] Christoph Freudenthaler, Lars Schmidt-Thieme, and Steffen Rendle. Bayesian factorization machines.
In NIPS workshop on Sparse Representation and Low-rank Approximation, 2011.
[3] Thorsten Joachims. Making large-scale support vector machine learning practical, pages 169–184. MIT
Press, Cambridge, MA, USA, 1999.
[4] Steffen Rendle. Factorization machines. In Proceedings of the 10th IEEE International Conference
on Data Mining. IEEE Computer Society, 2010.
[5] Steffen Rendle. Factorization machines with libFM. ACM Trans. Intell. Syst. Technol., 3(3):57:1–
57:22, May 2012.
[6] Steffen Rendle. Learning recommender systems with adaptive regularization. In WSDM ’12: Proceedings of the third ACM international conference on Web search and data mining, New York, NY,
USA, 2012. ACM.
[7] Steffen Rendle. Scaling factorization machines to relational data. In Proceedings of the 39th international conference on Very Large Data Bases, PVLDB’13, pages 337–348. VLDB Endowment,
2013.
[8] Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, and Lars Schmidt-Thieme. Fast contextaware recommendations with factorization machines. In Proceedings of the 34th ACM SIGIR Conference on Reasearch and Development in Information Retrieval. ACM, 2011.
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