pyTigerGraph GDS Metrics
Utility for gathering metrics for GNN predictions.
Accuracy
Accuracy = \(\sum_{i=1}^n (predictions_i == labels_i)/n\)
Usage:
-
Call the update function to add predictions and labels.
-
Get accuracy score at any point by accessing the value property.
BinaryRecall
This metric is deprecated. Use Recall instead.
Recall = \(\sum(predictions * labels)/\sum(labels)\)
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.
ConfusionMatrix
Updates a confusion matrix as new updates occur.
_init_()
init(num_classes: int) → None
Instantiate the Confusion Matrix metric.
Parameter:
-
num_classes (int): Number of classes in the classification task.
Recall
Recall = stem:[true positives/\sum(true positives + false negatives)}
This metric is for classification, i.e., both predictions and labels are arrays of multiple whole numbers.
Usage:
-
Call the update function to add predictions and labels.
-
Get recall score at any point by accessing the value property.
BinaryPrecision
This metric is deprecated. Use the Precision metric instead. Precision = \(\sum(predictions * labels)/\sum(predictions)\)
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.
Precision
Recall = stem:[true positives/\sum(true positives + false positives)
This metric is for classification, i.e., both predictions and labels are arrays of multiple whole numbers.
Usage:
-
Call the update function to add predictions and labels.
-
Get recall score at any point by accessing the value property.
MSE
MSE = \(\sum(predicted-actual)^2/n\)
This metric is for regression tasks, i.e. predicting a n-dimensional vector of float values.
Usage:
-
Call the update function to add predictions and labels.
-
Get MSE value at any point by accessing the value property.
RMSE
RMSE = \(\sqrt(\sum(predicted-actual)^2/n)\)
This metric is for regression tasks, i.e. predicting a n-dimensional vector of float values.
Usage:
-
Call the update function to add predictions and labels.
-
Get RMSE score at any point by accessing the value property.
MAE
MAE = \(\sum(predicted-actual)/n\)
This metric is for regression tasks, i.e. predicting a n-dimensional vector of float values.
Usage:
-
Call the update function to add predictions and labels.
-
Get MAE value at any point by accessing the value property.
HitsAtK
This metric is used in link prediction tasks, i.e. determining if two vertices have an edge between them. Also known as Precsion@K.
Usage:
-
Call the update function to add predictions and labels.
-
Get Hits@K value at any point by accessing the value property.
Parameters:
-
k (int): Top k number of entities to compare.
_init_()
init(k: int) → None
Instantiate the Hits@K Metric
Parameter:
-
k (int): Top k number of entities to compare.
RecallAtK
This metric is used in link prediction tasks, i.e. determining if two vertices have an edge between them
Usage:
-
Call the update function to add predictions and labels.
-
Get Recall@K value at any point by accessing the value property.
Parameters:
-
k (int): Top k number of entities to compare.
_init_()
init(k: int) → None
Instantiate the Recall@K Metric
Parameter:
-
k (int): Top k number of entities to compare.
ClassificationMetrics
Collects Loss, Accuracy, Precision, Recall, and Confusion Matrix Metrics.
_init_()
init(num_classes: int = 2)
Instantiate the Classification Metrics collection.
Parameter:
-
num_classes (int): Number of classes in the classification task.
update_metrics()
update_metrics(loss, out, batch, target_type = None)
Update the metrics collected.
Parameters:
loss (float): loss value to update out (ndarray): the predictions of the model batch (dict): the batch to calculate metrics on target_type (str, optional): the type of schema element to calculate the metrics for
RegressionMetrics
Collects Loss, MSE, RMSE, and MAE metrics.
update_metrics()
update_metrics(loss, out, batch, target_type = None)
Update the metrics collected.
Parameters:
loss (float): loss value to update out (ndarray): the predictions of the model batch (dict): the batch to calculate metrics on target_type (str, optional): the type of schema element to calculate the metrics for
LinkPredictionMetrics
Collects Loss, Recall@K, and Hits@K metrics.
_init_()
init(k)
Instantiate the Classification Metrics collection.
Parameter:
-
k (int): The number of results to look at when calculating metrics.
update_metrics()
update_metrics(loss, out, batch, target_type = None)
Update the metrics collected.
Parameters:
loss (float): loss value to update out (ndarray): the predictions of the model batch (dict): the batch to calculate metrics on target_type (str, optional): the type of schema element to calculate the metrics for