Recommenders built using the DRecPy framework follow the usual method definitions: fit() to fit the model to the provided data, and predict(), rank() or recommend() to provide predictions. Once trained, in order to evaluate a model, one can build custom evaluation processes, or can use the builtin ones, which are defined on the evaluation_docs.
Here's a quick example of training the CDAE recommender with the MovieLens-100k data set on 100 epochs, and evaluating the ranking performance on 100 test users. Node that a seed parameter is passed through when instantiating the CDAE object, as well as when calling the evaluation process, so that we can have a deterministic pipeline.
../../../examples/cdae.py
To learn more about the public methods offered by the InteractionDataset module, please read the respective api documentation. This section is simply a brief introduction on how to import and make use of data sets.
At the moment, DRecPy provides various builtin data sets, such as: the MovieLens (100k, 1M, 10M and 20M) and the Book Crossing data set. Whenever you're using a builtin data set for the first time, a new folder will be created at your home path called ".DRecPy_data". If you want to provide a custom path for saving these data sets, you can do so by providing the DATA_FOLDER environment variable mapping to the intended path.
The example bellow shows how to use a builtin data set and how to manipulate it using the provided methods:
../../../examples/integrated_datasets.py
Custom data sets are also supported, and you should provide the path to the csv file as well as the column names and the delimiter.
../../../examples/custom_datasets.py
Note that there are 3 required columns: user, item and interaction.