| Title: | An Implementation of Graph Net Architecture Based on 'torch' |
|---|---|
| Description: | Proposes a 'torch' implementation of Graph Net architecture allowing different options for message passing and feature embedding. |
| Authors: | Giancarlo Vercellino [aut, cre, cph] |
| Maintainer: | Giancarlo Vercellino <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.0 |
| Built: | 2024-10-25 06:03:01 UTC |
| Source: | https://github.com/cran/spinner |
Spinner is an implementation of Graph Nets based on torch. Graph Nets are a family of neural network architectures designed for processing graphs and other structured data. They consist of a set of message-passing operations, which propagate information between nodes and edges in the graph, and a set of update functions, which compute new node and edge features based on the received messages.
Proposes a 'torch' implementation of Graph Net architecture allowing different options for message passing and feature embedding.
spinner( graph, target, node_labels = NA, edge_labels = NA, context_labels = NA, direction = "from_head", sampling = NA, threshold = 0.01, method = "null", node_embedding_size = 5, edge_embedding_size = 5, context_embedding_size = 5, update_order = "enc", n_layers = 3, skip_shortcut = FALSE, forward_layer = 32, forward_activation = "relu", forward_drop = 0.3, mode = "sum", optimization = "adam", epochs = 100, lr = 0.01, patience = 30, weight_decay = 0.001, reps = 1, folds = 3, holdout = 0.2, verbose = TRUE, seed = 42 )spinner( graph, target, node_labels = NA, edge_labels = NA, context_labels = NA, direction = "from_head", sampling = NA, threshold = 0.01, method = "null", node_embedding_size = 5, edge_embedding_size = 5, context_embedding_size = 5, update_order = "enc", n_layers = 3, skip_shortcut = FALSE, forward_layer = 32, forward_activation = "relu", forward_drop = 0.3, mode = "sum", optimization = "adam", epochs = 100, lr = 0.01, patience = 30, weight_decay = 0.001, reps = 1, folds = 3, holdout = 0.2, verbose = TRUE, seed = 42 )
graph |
A graph in igraph format (without name index for nodes). |
target |
String. Predicted dimension. Options are: "node", "edge". |
node_labels |
String. Character vector with labels of node features. In case of absent features, default to NA (automatic node embedding with selected method). |
edge_labels |
String. Character vector with labels of edge features. In case of absent features, default to NA (automatic edge embedding with selected method). |
context_labels |
String. Character vector with labels of context features. In case of absent features, default to NA (automatic context embedding with selected method). |
direction |
String. Direction of message propagation. Options are: "from_head", "from_tail". Default to: "from_head". |
sampling |
Positive numeric or integer. In case of huge graph, you can opt for a subgraph. Sampling dimension expressed in absolute value or percentage. Default: NA (no sampling). |
threshold |
Numeric. Below this threshold (calculated on edge density), sampling is done on edges, otherwise on nodes. Default: 0.01. |
method |
String. Embedding method in case of absent features. Options are: "null" (zeroed tensor), "laplacian", "adjacency". Default: "null". |
node_embedding_size |
Integer. Size for node embedding. Default: 5. |
edge_embedding_size |
Integer. Size for edge embedding. Default: 5. |
context_embedding_size |
Integer. Size for node embedding. Default: 5. |
update_order |
String. The order of message passing through nodes (n), edges (e) and context (c) for updating information. Available options are: "enc", "nec", "cen", "ecn", "nce", "cne". Default: "enc". |
n_layers |
Integer. Number of graph net variant layers. Default: 1. |
skip_shortcut |
Logical. Flag for applying skip shortcut after the graph net variant layers. Default: FALSE. |
forward_layer |
Integer. Single integer vector with size for forward net layer. Default: 32 (layers with 32 nodes). |
forward_activation |
String. Single character vector with activation for forward net layer. Available options are: "linear", "relu", "mish", "leaky_relu", "celu", "elu", "gelu", "selu", "bent", "softmax", "softmin", "softsign", "sigmoid", "tanh". Default: "relu". |
forward_drop |
Numeric. Single numeric vector with drop out for forward net layer. Default: 0.3. |
mode |
String. Aggregation method for message passing. Options are: "sum", "mean", "max". Default: "sum". |
optimization |
String. Optimization method. Options are: "adadelta", "adagrad", "rmsprop", "rprop", "sgd", "asgd", "adam". |
epochs |
Positive integer. Default: 100. |
lr |
Positive numeric. Learning rate. Default: 0.01. |
patience |
Positive integer. Waiting time (in epochs) before evaluating the overfit performance. Default: 30. |
weight_decay |
Positive numeric. L2-Regularization weight. Default: 0.001. |
reps |
Positive integer. Number of repeated measures. Default: 1. |
folds |
Positive integer. Number of folds for each repetition. Default: 3. |
holdout |
Positive numeric. Percentage of nodes for testing (edges are computed accordingly). Default: 0.2. |
verbose |
Logical. Default: TRUE |
seed |
Random seed. Default: 42. |
This function returns a list including:
graph: analyzed graph is returned (original graph or sampled subgraph).
model_description: general model description.
model_summary: summary for each torch module.
pred_fun: function to predict on new graph data (you need to add new nodes/edges to the original graph respecting the directionality).
cv_error: cross-validation error for each repetition and each fold. The error is a weighted normalized loss based on mse and binary cross-entropy (depending on the nature of each specific feature).
summary_errors: final summary of error during cross-validation and testing.
history: plot with loss for final training and testing.
time_log: computation time.
Giancarlo Vercellino [email protected]
Maintainer: Giancarlo Vercellino [email protected] [copyright holder]
Useful links:
spinner_random_search is a function for fine-tuning using random search on the hyper-parameter space of spinner (predefined or custom).
spinner_random_search( n_samp, graph, target, node_labels = NA, edge_labels = NA, context_labels = NA, direction = NULL, sampling = NA, threshold = 0.01, method = NULL, node_embedding_size = NULL, edge_embedding_size = NULL, context_embedding_size = NULL, update_order = NULL, n_layers = NULL, skip_shortcut = NULL, forward_layer = NULL, forward_activation = NULL, forward_drop = NULL, mode = NULL, optimization = NULL, epochs = 100, lr = NULL, patience = 30, weight_decay = NULL, reps = 1, folds = 2, holdout = 0.2, verbose = TRUE, seed = 42, keep = FALSE )spinner_random_search( n_samp, graph, target, node_labels = NA, edge_labels = NA, context_labels = NA, direction = NULL, sampling = NA, threshold = 0.01, method = NULL, node_embedding_size = NULL, edge_embedding_size = NULL, context_embedding_size = NULL, update_order = NULL, n_layers = NULL, skip_shortcut = NULL, forward_layer = NULL, forward_activation = NULL, forward_drop = NULL, mode = NULL, optimization = NULL, epochs = 100, lr = NULL, patience = 30, weight_decay = NULL, reps = 1, folds = 2, holdout = 0.2, verbose = TRUE, seed = 42, keep = FALSE )
n_samp |
Positive integer. Number of models to be randomly generated sampling the hyper-parameter space. |
graph |
A graph in igraph format (without name index for nodes). |
target |
String. Predicted dimension. Options are: "node", "edge". |
node_labels |
String. Character vector with labels of node features. In case of absent features, default to NA (automatic node embedding with selected method). |
edge_labels |
String. Character vector with labels of edge features. In case of absent features, default to NA (automatic edge embedding with selected method). |
context_labels |
String. Character vector with labels of context features. In case of absent features, default to NA (automatic context embedding with selected method). |
direction |
String. Direction of message propagation. Options are: "from_head", "from_tail". Default to: "from_head". |
sampling |
Positive numeric or integer. In case of huge graph, you can opt for a subgraph. Sampling dimension expressed in absolute value or percentage. Default: NA (no sampling). |
threshold |
Numeric. Below this threshold (calculated on edge density), sampling is done on edges, otherwise on nodes. Default: 0.01. |
method |
String. Embedding method in case of absent features. Options are: "null" (zeroed tensor), "laplacian", "adjacency". Default: "null". |
node_embedding_size |
Integer. Size for node embedding. Default: 5. |
edge_embedding_size |
Integer. Size for edge embedding. Default: 5. |
context_embedding_size |
Integer. Size for node embedding. Default: 5. |
update_order |
String. The order of message passing through nodes (n), edges (e) and context (c) for updating information. Available options are: "enc", "nec", "cen", "ecn", "nce", "cne". Default: "enc". |
n_layers |
Integer. Number of graph net variant layers. Default: 1. |
skip_shortcut |
Logical. Flag for applying skip shortcut after the graph net variant layers. Default: FALSE. |
forward_layer |
Integer. Single integer vector with size for forward net layer. Default: 32 (layers with 32 nodes). |
forward_activation |
String. Single character vector with activation for forward net layer. Available options are: "linear", "relu", "mish", "leaky_relu", "celu", "elu", "gelu", "selu", "bent", "softmax", "softmin", "softsign", "sigmoid", "tanh". Default: "relu". |
forward_drop |
Numeric. Single numeric vector with drop out for forward net layer. Default: 0.3. |
mode |
String. Aggregation method for message passing. Options are: "sum", "mean", "max". Default: "sum". |
optimization |
String. Optimization method. Options are: "adadelta", "adagrad", "rmsprop", "rprop", "sgd", "asgd", "adam". |
epochs |
Positive integer. Default: 100. |
lr |
Positive numeric. Learning rate. Default: 0.01. |
patience |
Positive integer. Waiting time (in epochs) before evaluating the overfit performance. Default: 30. |
weight_decay |
Positive numeric. L2-Regularization weight. Default: 0.001. |
reps |
Positive integer. Number of repeated measures. Default: 1. |
folds |
Positive integer. Number of folds for each repetition. Default: 3. |
holdout |
Positive numeric. Percentage of nodes for testing (edges are computed accordingly). Default: 0.2. |
verbose |
Logical. Default: TRUE |
seed |
Random seed. Default: 42. |
keep |
Logical. Flag to TRUE to keep all the explored models. Default: FALSE. |
This function returns a list including:
random_search: summary of the sampled hyper-parameters and average error metrics.
best: best model according to overall ranking on all average error metrics (for negative metrics, absolute value is considered).
time_log: computation time.
all_models: list with all generated models (if keep flagged to TRUE).
Giancarlo Vercellino [email protected]
https://rpubs.com/giancarlo_vercellino/spinner