pyTigerGraph GDS Metrics

Utility for gathering metrics for GNN predictions.

Accuracy

Accuracy = sum(predictions == labels) / len(labels)

Usage:

• Call the update function to add predictions and labels.

• Get accuracy score at any point by accessing the value property.

update()

`update(preds: ndarray, labels: ndarray) → None`

Add predictions and labels to be compared.

Parameters:

preds (ndarray): Array of predicted labels. labels (ndarray): Array of true labels.

value()

`value() → float`

Get accuracy score.

Returns:

Accuracy score (float).

BinaryRecall

Recall = $rac{\sum(predictions * labels)}{\sum(labels)}$

This metric is for binary classifications, i.e., both predictions and labels are arrays of 0’s and 1’s.

Usage:

• Call the update function to add predictions and labels.

• Get recall score at any point by accessing the value property.

update()

`update(preds: ndarray, labels: ndarray) → None`

Add predictions and labels to be compared.

Parameters:

preds (ndarray): Array of predicted labels. labels (ndarray): Array of true labels.

value()

`value() → float`

Get recall score.

Returns:

Recall score (float).

BinaryPrecision

Precision = $rac{\sum(predictions * labels)}{\sum(predictions)}$

This metric is for binary classifications, i.e., both predictions and labels are arrays of 0’s and 1’s.

Usage:

• Call the update function to add predictions and labels.

• Get precision score at any point by accessing the value property.

update()

`update(preds: ndarray, labels: ndarray) → None`

Add predictions and labels to be compared.

Parameters:

preds (ndarray): Array of predicted labels. labels (ndarray): Array of true labels.

value()

`value() → float`

Get precision score.

Returns:

Precision score (float).