spinner_random_search {spinner}

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 [e-mail]

References

https://rpubs.com/giancarlo_vercellino/spinner