ClayRS 0.5.1 - ClayRS can see!

Release which includes image support for the Content Analyzer and RecSys modules!

• This release was co-developed with @m-elio

NOTE: The minimum Python version has been bumped up from Python 3.7 to Python 3.8 in order to use @functools.cached_property decorator

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Added

Content Analyzer

• Implemented visual preprocessors thanks to torchvision library
  • Also torch augmenters were implemented
  • All of them can be checked in the docs
• Implemented postprocessors techniques which also work for textual techniques
  • Visual bag of words (with count and tfidf weighting schema)
  • Scipy vector quantization
  • Dimensionality reduction techniques from sklearn (PCA, Gaussian random projections, Feature agglomeration)
• Images path to process specified in the raw source could be an absolute_path, relative_path, online url!
• Implemented several content techniques which extract embedding features from images
  • Pre-trained models from timm
  • Pre-trained caffe models using opencv.dnn
  • Hog descriptor, Canny edge detector, LBP, SIFT from skimage
  • Color histogram
  • Custom filter convolution
• Implemented FromNPY technique, which imports features from a numpy serialized matrix

RecSys

• Implemented VBPR technique following the corresponding paper
  • The implementation has been tested thoroughly by experimental comparison with cornac (experiment repository can be found here)

Changed

Content Analyzer

• Changed Ratings class to use numpy arrays and integer mappings instead of relying on python dictionaries and strings
• Adapted FieldContentProductionTechnique to consider the distinction between textual and visual techniques
• Added possibility to serialize contents produced with multi threading

RecSys

• Vectorized computation of CentroidVector algorithm
• Adapted content based algorithm abstraction to make room for neural algorithms
• Fixed missing Bootstrap partitioning technique from online documentation
• AllItemsMethodology by default now considers as items catalog the union between train and test set
• HoldOutPartitioningTechnique can now accept an integer value representing the n° of instances to hold rather than a percentage
• Changed log of users skipped in partitioning/algorithm fitting: a single print with total number of skipped users is fired instead of a single one for each skipped user

EvalModel

• Changed NDCG implementation to allow the choice of the gain weights (linear or exponential) and the definition of a discount function
• Improved visualization of statistical tests results

ClayRS 0.4.1

Added

• ClayRS can be now installed to Python 3.10 thanks to @m-elio!
  • Support is still guaranteed for Python 3.7 - 3.8 - 3.9

ClayRS 0.4.0

Added

• Implemented different weighting schemas for tuning the computation of the personalized page rank!
  • Check documentation
• Added the possibility to perform a complete experiment for a specific list of users

Changed

• Fixed missing __repr__ method to MAP metric
• Changed default number of cpus from 0 (all available) to 1 (single core) for the Experiment class in order to overcome, for naïve users, serialization limitation of python multiprocessing mechanism

ClayRS 0.3.1

Changed

• The query endpoint to DBPedia ontology has been changed from the one provided by factforge to the virtuoso one
• SPARQL query definition has been changed in order to improve response times
  • From ~20 min to ~6min to retrieve properties for all 1682 movies of the movielens 100k dataset
• Changed default number of cpus from 0 (all available) to 1 (single core) for the recsys phase in order to overcome, for naïve users, serialization limitation of python multiprocessing mechanism

ClayRS 0.3.0

Added

• Implemented ContentBasedExperiment and GraphBasedExperiment classes that lets you easily compare different algorithms with a simple interface!
  • Check documentation

Changed

• Fixed missing __str__ method to some Content Based algorithms
• Fixed error when property should be loaded in the graph but some items are missing from local
• Set default n_recs parameter for the rank methods of each RecSys to 10
• Fixed NoneType error for fit(), fit_rank(), fit_predict() methods of Content Based when the algorithm can't be fit for a user
• Fixed Exception never retrieved log of asyncio that appeared in only specific environments
• Re-organized internal module structure and imports for avoiding and preventing circular imports

ClayRS 0.2.1

Changed

• This is a hotfix for duplicate logging which may happen in specific scenarios (e.g. Google Colab)

ClayRS 0.2.0

Added

• Implemented multiprocessing via distex library for the recsys phase!
  • num_cpus parameter added to each eligible method (fit(), fit_rank(), rank(), predict(), etc.)
• Implemented MAP, MAP@K metrics for the evaluation module
• Implemented Bootstrap partitioning as an additional partitioning technique

Changed

• All algorithms are now able to handle duplicate interactions in the dataset
  • In case data augmentation has been performed, bootstrap partitioning has been used, etc.

ClayRS 0.1.3

Changed

• Changed behaviour of all metrics which need to compute popularity
  • Now there's no need to re-instantiate them in case multiple runs must be executed

Initial release

First release of ClayRS!