EvaluationCF.jl

Utility package for metrics, splitting and evaluate Collaborative Filtering datasets
Author JuliaRecsys
Popularity
0 Stars
Updated Last
1 Year Ago
Started In
October 2018

EvaluationCF.jl

Package for evaluation of predictive algorithms. It contains metrics, data partitioning and more.

Build Status

Installation

The package can be installed with the Julia package manager. From the Julia REPL, type ] to enter the Pkg REPL mode and run:

pkg> add EvaluationCF

Or, equivalently, via the Pkg API:

julia> import Pkg; Pkg.add("EvaluationCF")

Example

julia> using Persa

julia> using DatasetsCF

julia> using ModelBasedCF

julia> using EvaluationCF

julia> dataset = DatasetsCF.MovieLens()
Collaborative Filtering Dataset
- # users: 943
- # items: 1682
- # ratings: 100000
- Ratings Preference: [1, 2, 3, 4, 5]

julia> sample = EvaluationCF.HoldOut(dataset)

julia> for (ds_train, ds_test) in sample
           model = ModelBasedCF.RandomModel(ds_train)
           mae = EvaluationCF.mae(model, ds_test)
           rmse = EvaluationCF.rmse(model, ds_test)
           coverage = EvaluationCF.coverage(model, ds_test)
           text =
           """ Experiment:
               MAE: $(mae)
               RMSE: $(rmse)
               Coverage: $(coverage)
           """

           print(text)
       end
 Experiment:
    MAE: 1.5095490450954905
    RMSE: 1.9079140406216837
    Coverage: 1.0

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