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:
| RecSys Algorithm | Rating Prediction | Item Recommendation | Implicit Feedback |
|---|---|---|---|
| User Average | x | x | |
| Item Average | x | x | |
| Slope One | x | x | |
| User Based KNN | x | x | |
| Item Based KNN | x | x | |
| Funk's SVD | x | x | |
| Most Popular | x | ||
| ALS | x | x | |
| ALS with Conjugate Gradient | x | x | |
| BPR for Matrix Factorization | x | x |
Although pyRecLab can be compiled on most popular operating system, it has been tested on the following distributions.
| Operating System | Version |
|---|---|
| Ubuntu | 16.04 |
| CentOS | 6.4 |
| Mac OS X | 10.11 ( El Capitan ) |
| Mac OS X | 10.12 ( Sierra ) |
If you use this library, please cite:
@inproceedings{1706.06291v2, author = {Gabriel Sepulveda and Vicente Dominguez and Denis Parra}, title = {pyRecLab: A Software Library for Quick Prototyping of Recommender Systems}, year = {2017}, month = {August}, eprint = {arXiv:1706.06291v2}, keywords = {Recommender Systems, Software Development, Recommender Library, Python Library} }
pyRecLab can be installed directly using pip as follow:
pip install pyreclabpip3 install pyreclabIn case there is not a package available for your favorite operating system, you can build pyRecLab through the following steps:
1.- Before starting, verify you have libpython-dev, boost and cmake installed on your system. If not, install it through your distribution's package manager, as shown next.
$ sudo apt-get install libpython-dev
$ sudo apt-get install cmake
$ sudo apt-get install libboost-devNote: for Python 3.x, install libpython3-dev instead of libpython-dev.
$ yum install python-devel
$ yum install cmake
$ yum install boost-devel$ brew install cmake
$ brew install boost2.- Clone the source code of pyRecLab in a local directory.
$ git clone https://github.com/gasevi/pyreclab.git3.- Build the Python module ( default: Python 2.7 ).
$ cd pyreclab
$ cmake .
$ makeBy default, pyRecLab will be compiled for Python 2.7. If you want to build it for Python 3.x, you can execute the following steps:
$ cd pyreclab
$ cmake -DCMAKE_PYTHON_VERSION=3.x .
$ make4.- Install pyRecLab.
$ sudo make installpyRecLab provides the following classes for representing each of the recommendation algorithm currenly supported:
-
So, you can import any of them as follows:
>>> from pyreclab import <RecAlg>or import the entire module as you prefer
>>> import pyreclabDue to the different nature of each algorithm, their implementations can have some variations on its parameters. For this reason, each class is described in detail in the following sections.
- Instance creation
>>> obj = pyreclab.UserAvg( dataset = filename,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2 )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
- Training
>>> obj.train( progress = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| progress | optional | False | Show progress bar |
- Rating prediction
>>> prediction = obj.predict( userId, itemId )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| itemId | mandatory | N.A. | Item identifier |
- Top-N item recommendation
>>> ranking = obj.recommend( userId, topN, includeRated )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| topN | optional | 10 | Top N items to recommend |
| includeRated | optional | False | Include rated items in ranking generation |
- Testing and evaluation for prediction
>>> 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| output_file | optional | N.A. | Output file to write predictions |
- Testing for recommendation
>>> recommendationList, map, ndcg = obj.testrec( input_file = testset,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2,
topn = 10,
output_file = 'ranking.json',
relevance_threshold = 0,
includeRated = False )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| topn | optional | 10 | Top N items to recommend |
| output_file | optional | N.A. | Output file to write predictions |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| includeRated | optional | False | Include rated items in ranking generation |
- Precision
>>> precision = obj.precision( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Recall
>>> recall = obj.recall( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Area Under the ROC Curve (AUC)
>>> auc = obj.AUC( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Mean Average precision
>>> map = obj.MAP( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Normalized Discounted Cumulative Gain
>>> map = obj.nDCG( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Instance creation
>>> obj = pyreclab.ItemAvg( dataset = filename,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2 )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
- Training
>>> obj.train( progress = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| progress | optional | False | Show progress bar |
- Rating prediction
>>> prediction = obj.predict( userId, itemId )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| itemId | mandatory | N.A. | Item identifier |
- Top-N item recommendation
>>> ranking = obj.recommend( userId, topN, includeRated )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| topN | optional | 10 | Top N items to recommend |
| includeRated | optional | False | Include rated items in ranking generation |
- Testing and evaluation for prediction
>>> 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| output_file | optional | N.A. | Output file to write predictions |
- Testing for recommendation
>>> recommendationList, map, ndcg = obj.testrec( input_file = testset,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2,
topn = 10,
output_file = 'ranking.json',
relevance_threshold = 2,
includeRated = False )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| topn | optional | 10 | Top N items to recommend |
| output_file | optional | N.A. | Output file to write predictions |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| includeRated | optional | False | Include rated items in ranking generation |
- Precision
>>> precision = obj.precision( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Recall
>>> recall = obj.recall( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Area Under the ROC Curve (AUC)
>>> auc = obj.AUC( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Mean Average precision
>>> map = obj.MAP( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Normalized Discounted Cumulative Gain
>>> map = obj.nDCG( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Instance creation
>>> obj = pyreclab.SlopeOne( dataset = filename,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2 )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
- Training
>>> obj.train( progress = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| progress | optional | False | Show progress bar |
- Rating prediction
prediction = obj.predict( userId, itemId )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| itemId | mandatory | N.A. | Item identifier |
- Top-N item recommendation
>>> ranking = obj.recommend( userId, topN, includeRated )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| topN | optional | 10 | Top N items to recommend |
| includeRated | optional | False | Include rated items in ranking generation |
- Testing and evaluation for prediction
>>> 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| output_file | optional | N.A. | Output file to write predictions |
- Testing for recommendation
>>> recommendationList, map, ndcg = obj.testrec( input_file = testset,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2,
topn = 10,
output_file = 'ranking.json',
relevance_threshold = 2,
includeRated = False )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| topn | optional | 10 | Top N items to recommend |
| output_file | optional | N.A. | Output file to write predictions |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| includeRated | optional | False | Include rated items in ranking generation |
- Precision
>>> precision = obj.precision( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Recall
>>> recall = obj.recall( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Area Under the ROC Curve (AUC)
>>> auc = obj.AUC( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Mean Average precision
>>> map = obj.MAP( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Normalized Discounted Cumulative Gain
>>> map = obj.nDCG( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Instance creation
>>> obj = pyreclab.UserKnn( dataset = filename,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2 )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
- Training
>>> obj.train( knn, similarity, progress = False )| Parameter | Type | Default value | Valid values | Description |
|---|---|---|---|---|
| knn | optional | 10 | positive integer | K nearest neighbors |
| similarity | optional | 'pearson' | 'pearson', 'cosine' | Similarity metric |
| progress | optional | False | Show progress bar |
- Rating prediction
>>> prediction = obj.predict( userId, itemId )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| itemId | mandatory | N.A. | Item identifier |
- Top-N item recommendation
>>> ranking = obj.recommend( userId, topN, includeRated )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| topN | optional | 10 | Top N items to recommend |
| includeRated | optional | False | Include rated items in ranking generation |
- Testing and evaluation for prediction
>>> 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| output_file | optional | N.A. | Output file to write predictions |
- Testing for recommendation
>>> recommendationList, map, ndcg = obj.testrec( input_file = testset,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2,
topn = 10,
output_file = 'ranking.json',
relevance_threshold = 2,
includeRated = False )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| topn | optional | 10 | Top N items to recommend |
| output_file | optional | N.A. | Output file to write predictions |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| includeRated | optional | False | Include rated items in ranking generation |
- Precision
>>> precision = obj.precision( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Recall
>>> recall = obj.recall( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Area Under the ROC Curve (AUC)
>>> auc = obj.AUC( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Mean Average precision
>>> map = obj.MAP( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Normalized Discounted Cumulative Gain
>>> map = obj.nDCG( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Instance creation
>>> obj = pyreclab.ItemKnn( dataset = filename,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2 )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
- Training
>>> obj.train( knn, similarity, progress = False )| Parameter | Type | Default value | Valid values | Description |
|---|---|---|---|---|
| knn | optional | 10 | positive integer | K nearest neighbors |
| similarity | optional | 'pearson' | 'pearson', 'cosine' | Similarity metric |
| progress | optional | False | Show progress bar |
- Rating prediction
>>> prediction = obj.predict( userId, itemId )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| itemId | mandatory | N.A. | Item identifier |
- Top-N item recommendation
>>> ranking = obj.recommend( userId, topN, includeRated )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| topN | optional | 10 | Top N items to recommend |
| includeRated | optional | False | Include rated items in ranking generation |
- Testing and evaluation for prediction
>>> 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| output_file | optional | N.A. | Output file to write predictions |
- Testing for recommendation
>>> recommendationList, map, ndcg = obj.testrec( input_file = testset,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2,
topn = 10,
output_file = 'ranking.json',
relevance_threshold = 2,
includeRated = False )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| topn | optional | 10 | Top N items to recommend |
| output_file | optional | N.A. | Output file to write predictions |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| includeRated | optional | False | Include rated items in ranking generation |
- Precision
>>> precision = obj.precision( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Recall
>>> recall = obj.recall( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Area Under the ROC Curve (AUC)
>>> auc = obj.AUC( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Mean Average precision
>>> map = obj.MAP( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Normalized Discounted Cumulative Gain
>>> map = obj.nDCG( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Instance creation
>>> obj = pyreclab.SVD( factors = 1000,
dataset = filename,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2 )| Parameter | Type | Default value | Description |
|---|---|---|---|
| factors | optional | 1000 | Number of latent factors in matrix factorization |
| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
- Training
>>> obj.train( maxiter = 100, lr = 0.01, lamb = 0.1, progress = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| maxiter | optional | 100 | Maximum number of iterations reached without convergence |
| lr | optional | 0.01 | Learning rate |
| lamb | optional | 0.1 | Regularization parameter |
| progress | optional | False | Show progress bar |
- Rating prediction
>>> prediction = obj.predict( userId, itemId )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| itemId | mandatory | N.A. | Item identifier |
- Top-N item recommendation
>>> ranking = obj.recommend( userId, topN, includeRated )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| topN | optional | 10 | Top N items to recommend |
| includeRated | optional | False | Include rated items in ranking generation |
- Testing and evaluation for prediction
>>> 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| output_file | optional | N.A. | Output file to write predictions |
- Testing for recommendation
>>> recommendationList, map, ndcg = obj.testrec( input_file = testset,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2,
topn = 10,
output_file = 'ranking.json',
relevance_threshold = 2,
includeRated = False )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| topn | optional | 10 | Top N items to recommend |
| output_file | optional | N.A. | Output file to write predictions |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| includeRated | optional | False | Include rated items in ranking generation |
- Precision
>>> precision = obj.precision( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Recall
>>> recall = obj.recall( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Area Under the ROC Curve (AUC)
>>> auc = obj.AUC( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Mean Average precision
>>> map = obj.MAP( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Normalized Discounted Cumulative Gain
>>> map = obj.nDCG( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Loss
>>> current_loss = obj.loss()- Reset factors
>>> obj.reset()- Instance creation
>>> obj = pyreclab.MostPopular( dataset = filename,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2 )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
- Training
>>> obj.train( progress = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| progress | optional | False | Show progress bar |
- Top-N item recommendation
>>> ranking = obj.recommend( userId, topN, includeRated )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| topN | optional | 10 | Top N items to recommend |
| includeRated | optional | False | Include rated items in ranking generation |
- Testing for recommendation
>>> recommendationList, map, ndcg = obj.testrec( input_file = testset,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2,
topn = 10,
output_file = 'ranking.json',
relevance_threshold = 2,
includeRated = False )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| output_file | optional | N.A. | Output file to write rankings |
| topN | optional | 10 | Top N items to recommend |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| includeRated | optional | False | Include rated items in ranking generation |
- Precision
>>> precision = obj.precision( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Recall
>>> recall = obj.recall( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Area Under the ROC Curve (AUC)
>>> auc = obj.AUC( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Mean Average precision
>>> map = obj.MAP( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Normalized Discounted Cumulative Gain
>>> map = obj.nDCG( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Instance creation
>>> obj = pyreclab.IFAls( factors = 50,
dataset = filename,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
observationcol = 2 )| Parameter | Type | Default value | Description |
|---|---|---|---|
| factors | optional | 50 | Number of latent factors in matrix factorization |
| 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 |
| observationcol | optional | 2 | Observation column position in dataset file |
- Training
>>> obj.train( maxiter, lambd, progress = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| alsNumIter | optional | 5 | Number of iterations in ALS algorithm |
| lambd | optional | 10 | Regularization parameter |
| progress | optional | False | Show progress bar |
- Top-N item recommendation
>>> ranking = obj.recommend( userId, topN, includeRated )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| topN | optional | 10 | Top N items to recommend |
| includeRated | optional | False | Include rated items in ranking generation |
- Testing for recommendation
>>> recommendationList, map, ndcg = obj.testrec( input_file = testset,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2,
topn = 10,
output_file = 'ranking.json',
relevance_threshold = 2,
includeRated = False )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| output_file | optional | N.A. | Output file to write rankings |
| topN | optional | 10 | Top N items to recommend |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| includeRated | optional | False | Include rated items in ranking generation |
- Precision
>>> precision = obj.precision( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Recall
>>> recall = obj.recall( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Area Under the ROC Curve (AUC)
>>> auc = obj.AUC( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Mean Average precision
>>> map = obj.MAP( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Normalized Discounted Cumulative Gain
>>> map = obj.nDCG( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Reset factors
>>> obj.reset()- Instance creation
>>> obj = pyreclab.IFAlsConjugateGradient( factors = 50,
dataset = filename,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
observationcol = 2 )| Parameter | Type | Default value | Description |
|---|---|---|---|
| factors | optional | 50 | Number of latent factors in matrix factorization |
| 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 |
| observationcol | optional | 2 | Observation column position in dataset file |
- Training
>>> obj.train( alsNumIter, lambd, cgNumIter, progress = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| alsNumIter | optional | 5 | Number of iterations in ALS algorithm |
| lambd | optional | 10 | Regularization parameter |
| cgNumIter | optional | 2 | Number of iterations in Conjugate Gradient algorithm |
| progress | optional | False | Show progress bar |
- Top-N item recommendation
>>> ranking = obj.recommend( userId, topN, includeRated )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| topN | optional | 10 | Top N items to recommend |
| includeRated | optional | False | Include rated items in ranking generation |
- Testing for recommendation
>>> recommendationList, map, ndcg = obj.testrec( input_file = testset,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2,
topn = 10,
output_file = 'ranking.json',
relevance_threshold = 2,
includeRated = False )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| output_file | optional | N.A. | Output file to write rankings |
| topN | optional | 10 | Top N items to recommend |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| includeRated | optional | False | Include rated items in ranking generation |
- Precision
>>> precision = obj.precision( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Recall
>>> recall = obj.recall( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Area Under the ROC Curve (AUC)
>>> auc = obj.AUC( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Mean Average precision
>>> map = obj.MAP( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Normalized Discounted Cumulative Gain
>>> map = obj.nDCG( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Reset factors
>>> obj.reset()- Instance creation
>>> obj = pyreclab.BprMf( factors = 20,
dataset = filename,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
observationcol = 2 )| Parameter | Type | Default value | Description |
|---|---|---|---|
| factors | optional | 20 | Number of latent factors in matrix factorization |
| 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 |
| observationcol | optional | 2 | Observation column position in dataset file |
- Training
>>> obj.train( maxiter = 100,
lr = 0.1,
lambda_w = 0.01,
lambda_hp = 0.01,
lambda_hm = 0.01,
progress = True )| Parameter | Type | Default value | Description |
|---|---|---|---|
| maxiter | optional | 100 | Number of iterations |
| lr | optional | 0.1 | Learning rate |
| lambda_w | optional | 0.01 | Regularization parameter for the user features |
| lambda_hp | optional | 0.01 | Regularization parameter for the item features and positive updates |
| lambda_hm | optional | 0.01 | Regularization parameter for the item features and negative updates |
| progress | optional | False | Show progress bar |
- Top-N item recommendation
>>> ranking = obj.recommend( userId, topN, includeRated )| Parameter | Type | Default value | Description |
|---|---|---|---|
| userId | mandatory | N.A. | User identifier |
| topN | optional | 10 | Top N items to recommend |
| includeRated | optional | False | Include rated items in ranking generation |
- Testing for recommendation
>>> recommendationList, map, ndcg = obj.testrec( input_file = testset,
dlmchar = b'\t',
header = False,
usercol = 0,
itemcol = 1,
ratingcol = 2,
topn = 10,
output_file = 'ranking.json',
relevance_threshold = 2,
includeRated = False )| 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 |
| ratingcol | optional | 2 | Rating column position in dataset file |
| output_file | optional | N.A. | Output file to write rankings |
| topN | optional | 10 | Top N items to recommend |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| includeRated | optional | False | Include rated items in ranking generation |
- Precision
>>> precision = obj.precision( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Recall
>>> recall = obj.recall( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Area Under the ROC Curve (AUC)
>>> auc = obj.AUC( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Mean Average precision
>>> map = obj.MAP( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Normalized Discounted Cumulative Gain
>>> map = obj.nDCG( user_id,
retrieved,
topn = 10,
relevance_threshold = 0,
include_rated = False )| Parameter | Type | Default value | Description |
|---|---|---|---|
| user_id | mandatory | N.A. | User identifier |
| retrieved | optional | N.A. | Recommendation list for user 'user_id' |
| topn | optional | 10 | Top N items to recommend. If 'retrieved' is provided, this value will be set to 'retrieved' length |
| relevance_threshold | optional | 0 | Lower threshold to consider an item as relevant ( threshold value included ) |
| include_rated | optional | False | Include rated items in ranking generation |
- Loss
>>> current_loss = obj.loss()- Reset factors
>>> obj.reset()- Add Windows support.
- Multi-threading.