elliot.recommender.neural.UserAutoRec package

Submodules

elliot.recommender.neural.UserAutoRec.userautorec module

Module description:

class elliot.recommender.neural.UserAutoRec.userautorec.UserAutoRec(data, config, params, *args, **kwargs)

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

AutoRec: Autoencoders Meet Collaborative Filtering (User-based)

For further details, please refer to the paper

Parameters

• hidden_neuron – List of units for each layer

• lr – Learning rate

• l_w – Regularization coefficient

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  UserAutoRec:
    meta:
      save_recs: True
    epochs: 10
    batch_size: 512
    hidden_neuron: 500
    lr: 0.0001
    l_w: 0.001

property name

train()

elliot.recommender.neural.UserAutoRec.userautorec_model module

Module description:

class elliot.recommender.neural.UserAutoRec.userautorec_model.Decoder(*args, **kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

call(inputs, **kwargs)

class elliot.recommender.neural.UserAutoRec.userautorec_model.Encoder(*args, **kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

call(inputs, training=None)

class elliot.recommender.neural.UserAutoRec.userautorec_model.UserAutoRecModel(*args, **kwargs)

Bases: tensorflow.python.keras.engine.training.Model

call(inputs, training=None, **kwargs)

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns

Python dictionary.

get_top_k(preds, train_mask, k=100)

predict(inputs, training=False, **kwargs)

Get full predictions on the whole users/items matrix.

Returns

The matrix of predicted values.

train_step(batch)

Module contents

Module description: