| Type: | Package |
| Title: | An Implementation of Graph Net Architecture Based on 'torch' |
| Version: | 1.1.0 |
| Maintainer: | Giancarlo Vercellino <giancarlo.vercellino@gmail.com> |
| Description: | Proposes a 'torch' implementation of Graph Net architecture allowing different options for message passing and feature embedding. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| Imports: | torch (≥ 0.9.0), igraph (≥ 1.3.5), purrr (≥ 0.3.4), ggplot2 (≥ 3.3.6), ggthemes (≥ 4.2.4), tictoc (≥ 1.0.1), readr (≥ 2.1.2), lubridate (≥ 1.7.10), rlist (≥ 0.4.6.2), fastDummies (≥ 1.6.3), entropy (≥ 1.3.1), abind (≥ 1.4-5) |
| URL: | https://rpubs.com/giancarlo_vercellino/spinner |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2023-03-21 05:57:46 UTC; gvercellino |
| Author: | Giancarlo Vercellino [aut, cre, cph] |
| Repository: | CRAN |
| Date/Publication: | 2023-03-21 06:10:02 UTC |
spinner
Description
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.
Usage
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
)
Arguments
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. |
Value
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.
Author(s)
Giancarlo Vercellino giancarlo.vercellino@gmail.com
Maintainer: Giancarlo Vercellino giancarlo.vercellino@gmail.com [copyright holder]
See Also
Useful links:
spinner_random_search
Description
spinner_random_search is a function for fine-tuning using random search on the hyper-parameter space of spinner (predefined or custom).
Usage
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
)
Arguments
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. |
Value
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).
Author(s)
Giancarlo Vercellino giancarlo.vercellino@gmail.com
References
https://rpubs.com/giancarlo_vercellino/spinner