elliot.recommender.adversarial.AMR package

Submodules

elliot.recommender.adversarial.AMR.AMR module

Module description:

class elliot.recommender.adversarial.AMR.AMR.AMR(data, config, params, *args, **kwargs)

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

Adversarial Multimedia Recommender

For further details, please refer to the paper

The model support two adversarial perturbations methods:

FGSM-based presented by X. He et al in paper <https://arxiv.org/pdf/1809.07062.pdf>

MSAP presented by Anelli et al. in paper <https://journals.flvc.org/FLAIRS/article/view/128443>

Parameters

• meta – eval_perturbations: If True Elliot evaluates the effects of both FGSM and MSAP perturbations for each validation epoch

• factors – Number of latent factor

• factors_d – Image-feature dimensionality

• lr – Learning rate

• l_w – Regularization coefficient

• l_b – Regularization coefficient of bias

• l_e – Regularization coefficient of image matrix embedding

• eps – Perturbation Budget

• l_adv – Adversarial regularization coefficient

• adversarial_epochs – Adversarial epochs

• eps_iter – Size of perturbations in MSAP perturbations

• nb_iter – Number of Iterations in MSAP perturbations

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

models:
  AMR:
    meta:
      save_recs: True
      eval_perturbations: True
    epochs: 10
    batch_size: 512
    factors: 200
    factors_d: 20
    lr: 0.001
    l_w: 0.1
    l_b: 0.001
    l_e: 0.1
    eps: 0.1
    l_adv: 0.001
    adversarial_epochs: 5
    eps_iter: 0.00001
    nb_iter: 20
    nb_iter: 20
    eps_iter: 0.00001  # If not specified = 2.5*eps/nb_iter

evaluate_perturbations(it=None)

get_recommendations(k, delta_features=None)

get_results()

property name

store_perturbation_results()

train()

elliot.recommender.adversarial.AMR.AMR_model module

Module description:

class elliot.recommender.adversarial.AMR.AMR_model.AMR_model(*args, **kwargs)

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

build_msap_perturbation(batch, eps_iter, nb_iter, delta_f=None)

Evaluate Adversarial Perturbation with MSAP https://journals.flvc.org/FLAIRS/article/view/128443

build_perturbation(batch, delta_f=None)

Evaluate Adversarial Perturbation with FGSM-like Approach

call(inputs, adversarial=False, training=None)

Calls the model on new inputs.

In this case call just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Parameters

• inputs – A tensor or list of tensors.

• training – Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.

• mask – A mask or list of masks. A mask can be either a tensor or None (no mask).

Returns

A tensor if there is a single output, or a list of tensors if there are more than one outputs.

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)

init_delta_f()

predict_item_batch(start, stop, start_item, stop_item, feat, delta_features)

train_step(batch, use_adv_train=False)

The logic for one training step.

This method can be overridden to support custom training logic. This method is called by Model.make_train_function.

This method should contain the mathemetical logic for one step of training. This typically includes the forward pass, loss calculation, backpropagation, and metric updates.

Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_train_function, which can also be overridden.

Parameters

data – A nested structure of `Tensor`s.

Returns

A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the values of the Model’s metrics are returned. Example: {‘loss’: 0.2, ‘accuracy’: 0.7}.

Module contents

Module description: