Calculate Network Prediction Performance Metrics

Source: R/network_evaluation.R

Calculates comprehensive performance metrics for evaluating predicted network structures, including classification performance, precision-recall metrics, and network topology metrics.

calculate_metrics(
  network_table,
  ground_truth,
  metric_type = c("all", "auc", "auroc", "auprc", "precision", "recall", "f1",
    "accuracy", "si", "ji"),
  return_plot = FALSE,
  line_color = "#1563cc",
  line_width = 1
)

Arguments

network_table

A data frame of predicted network structure containing:

  • regulator - Source nodes of the network edges

  • target - Target nodes of the network edges

  • weight - Edge weights representing prediction confidence

ground_truth

A data frame of ground truth network with the same format as network_table.

metric_type

The type of metric to return, default is all. This can take any of the following choices:

  • all - Returns all available metrics with Performance Metrics plot

  • auc - Returns both AUROC and AUPRC with their plots

  • auroc - Area Under ROC Curve with plot

  • auprc - Area Under Precision-Recall Curve with plot

  • precision - Proportion of correct predictions among positive predictions

  • recall - Proportion of actual positives correctly identified

  • f1 - Harmonic mean of precision and recall

  • accuracy - Overall classification accuracy

  • si - Set Intersection, counting correctly predicted edges

  • ji - Jaccard Index, measuring overlap between predicted and true networks

return_plot

Logical value, default is FALSE, whether to generate visualization plots

line_color

Color for plot lines, default is #1563cc

line_width

Width for plot lines, default is 1

Value

A list containing:

  • metrics - A data frame with requested metrics

  • plot - A plot object if return_plot = TRUE (optional)

Examples

data("example_matrix")
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
#> ℹ Running for <dense matrix>.
#> ℹ Checking input parameters.
#> ℹ Using L0 sparse regression model.
#> ℹ Using 1 core
#> ✔ Run done.
calculate_metrics(
  network_table,
  example_ground_truth,
  return_plot = TRUE
)
#> $metrics
#>      Metric  Value
#> 1     AUROC  0.952
#> 2     AUPRC  0.437
#> 3 Precision  0.529
#> 4    Recall  1.000
#> 5        F1  0.692
#> 6       ACC  0.948
#> 7        JI  0.514
#> 8        SI 18.000
#> 
#> $plot

#> 
calculate_metrics(
  network_table,
  example_ground_truth,
  metric_type = "auroc"
)
#> $metrics
#>   Metric Value
#> 1  AUROC 0.952
#>