PyTorch Models¶
In order to have more flexibility in the use of neural network models, these are directly assessible as torch.nn.Module, using the extensions .model, for example:
>>> from cdt.causality.graph.model.CGNN
to import the CGNN Pytorch model. The available models are the following:
CGNN
SAM
NCC
GNN
FSGNN
CGNN¶

class
cdt.causality.graph.model.
CGNN_model
(adj_matrix, batch_size, nh=20, device=None, confounding=False, initial_graph=None, **kwargs)[source]¶ Class defining the CGNN model.
 Parameters
adj_matrix (numpy.array) – Adjacency Matrix of the model to evaluate
batch_size (int) – Minibatch size. ~500 is recommended
nh (int) – number of hidden units in the hidden layers
device (str) – device to which the computation is to be made
confounding (bool) – Enables the confounding variant
initial_graph (numpy.array) – Initial graph in the confounding case.

forward
()[source]¶ Generate according to the topological order of the graph, outputs a batch of generated data of size batch_size.
 Returns
Generated data
 Return type
torch.Tensor

run
(dataset, train_epochs=1000, test_epochs=1000, verbose=None, idx=0, lr=0.01, dataloader_workers=0, **kwargs)[source]¶ Run the CGNN on a given graph.
 Parameters
dataset (torch.utils.data.Dataset) – True Data, on the same device as the model.
train_epochs (int) – number of train epochs
test_epochs (int) – number of test epochs
verbose (bool) – verbosity of the model
idx (int) – indicator for printing purposes
lr (float) – learning rate of the model
dataloader_workers (int) – number of workers
 Returns
Average score of the graph on test_epochs epochs
 Return type
float
SAM¶

class
cdt.causality.graph.model.
SAM_generators
(data_shape, nh, skeleton=None, cat_sizes=None, linear=False, numberHiddenLayersG=1)[source]¶ Ensemble of all the generators.

class
cdt.causality.graph.model.
SAM_discriminator
(nfeatures, dnh, numberHiddenLayersD=2, mask=None)[source]¶ SAM discriminator.

forward
(input, obs_data=None)[source]¶ Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

NCC¶
GNN¶

class
cdt.causality.pairwise.model.
GNN_model
(batch_size, nh=20, lr=0.01, train_epochs=1000, test_epochs=1000, idx=0, verbose=None, dataloader_workers=0, **kwargs)[source]¶ Torch model for the GNN structure.
 Parameters
batch_size (int) – size of the batch going to be fed to the model
nh (int) – Number of hidden units in the hidden layer
lr (float) – Learning rate of the Model
train_epochs (int) – Number of train epochs
test_epochs (int) – Number of test epochs
idx (int) – Index (for printing purposes)
verbose (bool) – Verbosity of the model
dataloader_workers (int) – Number of workers for dataset loading
device (str) – device on with the algorithm is going to be run on
FSGNN¶

class
cdt.independence.graph.model.
FSGNN_model
(sizes, dropout=0.0, activation_function=<class 'torch.nn.modules.activation.ReLU'>)[source]¶ Variant of CGNN for feature selection.
 Parameters
sizes (list) – Size of the neural network layers
dropout (float) – Dropout rate of the neural connections
activation_function (torch.nn.Module) – Activation function of the network

forward
(x)[source]¶ Forward pass in the network.
 Parameters
x (torch.Tensor) – input data
 Returns
output of the network
 Return type
torch.Tensor

train
(dataset, lr=0.01, l1=0.1, batch_size= 1, train_epochs=1000, test_epochs=1000, device=None, verbose=None, dataloader_workers=0)[source]¶ Train the network and output the scores of the features
 Parameters
dataset (torch.utils.data.Dataset) – Original data
lr (float) – Learning rate
l1 (float) – Coefficient of the L1 regularization
batch_size (int) – Batch size of the model, defaults to the dataset size.
train_epochs (int) – Number of train epochs
test_epochs (int) – Number of test epochs
device (str) – Device on which the computation is to be run
verbose (bool) – Verbosity of the model
dataloader_workers (int) – Number of workers for dataset loading
 Returns
feature selection scores for each feature.
 Return type
list