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Pyreclab: Recommendation lab for Python

Pyreclab is a recommendation library designed for training recommendation models with a friendly and easy-to-use interface, keeping a good performance in memory and CPU usage.

In order to achieve this, Pyreclab is built as a Python module to give a friendly access to its algorithms and it is completely developed in C++ to avoid the lack of performace of the interpreted languages.

At this moment, the following recommendation algorithms are supported:

Rating Prediction

  • User Avgerage
  • Item Average
  • Slope One
  • User Based KNN
  • Item Based KNN
  • Funk's SVD

Item Recommendation

  • Most Popular

Although Pyreclab can be compiled on most popular operating system, it has been tested on Linux Ubuntu 16.04.

Build and install

1.- Before start, verify you have libboost-dev installed on your system. If not, install it using your favorite package manager.

$ sudo apt-get install libboost-dev

2.- Clone the source code of Pyreclab in a local directory.

$ git clone https://github.com/gasevi/pyreclab.git

3.- Build the Python module ( default: Python 2.7 ).

$ cd pyreclab
$ cmake .
$ make

By default, it will be compiled for Python 2.7. If you want to build it for Python 3, you can execute the following steps:

$ cd pyreclab
$ cmake -DPY_MAJOR_VERSION=3 .
$ make

4.- Install Pyreclab.

$ make install

API Documentation

Pyreclab provides the following classes for representing each of the recommendation algorithm currenly supported:

>>> from pyreclab import <RecAlg>

or import the entire module as you prefer

>>> import pyreclab

Afer that, to create an instance of any of these clases, you must provide a dataset file with the training information, which must contain the fields user_id, item_id and rating.

The following example shows the generic format for creating one of these instances.

>>> obj = pyreclab.RecAlg( dataset = filename,
                           dlmchar = b'\t',
                           header = False,
                           usercol = 0,
                           itemcol = 1,
                           ratingcol = 2 )

Where RecAlg represents the recommendation algorithm chosen from the previous list, and its parameters are presented in the next table.

Parameter Type Default value Description
dataset mandatory N.A. Dataset filename with fields: userid, itemid and rating
dlmchar optional tab Delimiter character between fields (userid, itemid, rating)
header optional False Whether dataset filename contains a header line to skip
usercol optional 0 User column position in dataset file
itemcol optional 1 Item column position in dataset file
rating optional 2 Rating column position in dataset file

Due to the different nature of each algorithm, their train methods can have different parameters. For this reason, they have been described for each class as shown below.

pyreclab.UserAvg

  • Training
>>> obj.train()
  • Rating prediction
>>> prediction = obj.predict( userId, itemId )
Parameter Type Default value Description
userId mandatory N.A. User identifier
itemId mandatory N.A. Item identifier
  • Testing and evaluation
>>> predictionList, mae, rmse = obj.test( input_file = testset,
                                          dlmchar = b'\t',
                                          header = False,
                                          usercol = 0,
                                          itemcol = 1,
                                          ratingcol = 2,
                                          output_file = 'predictions.csv' )
Parameter Type Default value Description
input_file mandatory N.A. Testset filename
dlmchar optional tab Delimiter character between fields (userid, itemid, rating)
header optional False Dataset filename contains first line header to skip
usercol optional 0 User column position in dataset file
itemcol optional 1 Item column position in dataset file
rating optional 2 Rating column position in dataset file
output_file optional N.A. Output file to write predictions

pyreclab.ItemAvg

  • Training
>>> obj.train()
  • Rating prediction
>>> prediction = obj.predict( userId, itemId )
Parameter Type Default value Description
userId mandatory N.A. User identifier
itemId mandatory N.A. Item identifier
  • Testing and evaluation
>>> predictionList, mae, rmse = obj.test( input_file = testset,
                                          dlmchar = b'\t',
                                          header = False,
                                          usercol = 0,
                                          itemcol = 1,
                                          ratingcol = 2,
                                          output_file = 'predictions.csv' )
Parameter Type Default value Description
input_file mandatory N.A. Testset filename
dlmchar optional tab Delimiter character between fields (userid, itemid, rating)
header optional False Dataset filename contains first line header to skip
usercol optional 0 User column position in dataset file
itemcol optional 1 Item column position in dataset file
rating optional 2 Rating column position in dataset file
output_file optional N.A. Output file to write predictions

pyreclab.SlopeOne

  • Training
obj.train()
  • Rating prediction
prediction = obj.predict( userId, itemId )
Parameter Type Default value Description
userId mandatory N.A. User identifier
itemId mandatory N.A. Item identifier
  • Testing and evaluation
predictionList, mae, rmse = obj.test( input_file = testset,
                                      dlmchar = b'\t',
                                      header = False,
                                      usercol = 0,
                                      itemcol = 1,
                                      ratingcol = 2,
                                      output_file = 'predictions.csv' )
Parameter Type Default value Description
input_file mandatory N.A. Testset filename
dlmchar optional tab Delimiter character between fields (userid, itemid, rating)
header optional False Dataset filename contains first line header to skip
usercol optional 0 User column position in dataset file
itemcol optional 1 Item column position in dataset file
rating optional 2 Rating column position in dataset file
output_file optional N.A. Output file to write predictions

pyreclab.UserKnn

  • Training
>>> obj.train( knn )
Parameter Type Default value Description
knn optional 10 K nearest neighbors
  • Rating prediction
>>> prediction = obj.predict( userId, itemId )
Parameter Type Default value Description
userId mandatory N.A. User identifier
itemId mandatory N.A. Item identifier
  • Testing and evaluation
>>> predictionList, mae, rmse = obj.test( input_file = testset,
                                          dlmchar = b'\t',
                                          header = False,
                                          usercol = 0,
                                          itemcol = 1,
                                          ratingcol = 2,
                                          output_file = 'predictions.csv' )
Parameter Type Default value Description
input_file mandatory N.A. Testset filename
dlmchar optional tab Delimiter character between fields (userid, itemid, rating)
header optional False Dataset filename contains first line header to skip
usercol optional 0 User column position in dataset file
itemcol optional 1 Item column position in dataset file
rating optional 2 Rating column position in dataset file
output_file optional N.A. Output file to write predictions

pyreclab.ItemKnn

  • Training
>>> obj.train( knn )
Parameter Type Default value Description
knn optional 10 K nearest neighbors
  • Rating prediction
>>> prediction = obj.predict( userId, itemId )
Parameter Type Default value Description
userId mandatory N.A. User identifier
itemId mandatory N.A. Item identifier
  • Testing and evaluation
>>> predictionList, mae, rmse = obj.test( input_file = testset,
                                          dlmchar = b'\t',
                                          header = False,
                                          usercol = 0,
                                          itemcol = 1,
                                          ratingcol = 2,
                                          output_file = 'predictions.csv' )
Parameter Type Default value Description
input_file mandatory N.A. Testset filename
dlmchar optional tab Delimiter character between fields (userid, itemid, rating)
header optional False Dataset filename contains first line header to skip
usercol optional 0 User column position in dataset file
itemcol optional 1 Item column position in dataset file
rating optional 2 Rating column position in dataset file
output_file optional N.A. Output file to write predictions

pyreclab.SVD

  • Training
>>> obj.train( factors = 1000, maxiter = 100, lr = 0.01, lamb = 0.1 )
Parameter Type Default value Description
factors optional 1000 Number of latent factors in matrix factorization
maxiter optional 100 Maximum number of iterations reached without convergence
lr optional 0.01 Learning rate
lamb optional 0.1 Regularization parameter
  • Rating prediction
>>> prediction = obj.predict( userId, itemId )
Parameter Type Default value Description
userId mandatory N.A. User identifier
itemId mandatory N.A. Item identifier
  • Testing and evaluation
>>> predictionList, mae, rmse = obj.test( input_file = testset,
                                          dlmchar = b'\t',
                                          header = False,
                                          usercol = 0,
                                          itemcol = 1,
                                          ratingcol = 2,
                                          output_file = 'predictions.csv' )
Parameter Type Default value Description
input_file mandatory N.A. Testset filename
dlmchar optional tab Delimiter character between fields (userid, itemid, rating)
header optional False Dataset filename contains first line header to skip
usercol optional 0 User column position in dataset file
itemcol optional 1 Item column position in dataset file
rating optional 2 Rating column position in dataset file
output_file optional N.A. Output file to write predictions

pyreclab.MostPopular

  • Training
>>> obj.train( topN )
Parameter Type Default value Description
topN optional 10 Top N items to recommend
  • Rating prediction
>>> ranking = obj.recommend( userId )
Parameter Type Default value Description
userId mandatory N.A. User identifier
  • Testing and evaluation
>>> predictionList, mae, rmse = obj.test( input_file = testset,
                                          dlmchar = b'\t',
                                          header = False,
                                          usercol = 0,
                                          output_file = 'predictions.csv' )
Parameter Type Default value Description
input_file mandatory N.A. Testset filename
dlmchar optional tab Delimiter character between fields (userid, itemid, rating)
header optional False Dataset filename contains first line header to skip
usercol optional 0 User column position in dataset file
output_file optional N.A. Output file to write predictions

On roadmap

  • Extend items recommendation methods to rating prediction algorithms.
  • Add ranking evaluation metrics.
  • Extend support for other operating systems like Mac OS X and Windows.

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