BaseMetricsTracker
Overview¶
A metrics_tracker
is a component that logs metrics relative to a machine learning workflow over time. Metrics tracking may be tightly related to metadata tracking, or an entirely different component. To allow for flexibility of implementation, the design of the base class is such that a standalone component should be able to be utilized, even though many may opt to leverage a more tightly integrated solution.
Attributes¶
BaseMetricsTracker
contains the following default attributes:
url
: The url of the metrics tracker.
Configuration¶
Required Configuration¶
BaseMetricsTracker
has no required configuration.
Interface¶
log_metric¶
Logs a metric with the corresponding resource ID, metric ID, and metric value.
def log_metric(self, resource, id, value, *args, **kwargs)
Arguments:
resource
(object): A resource object.id
(str): A unique identifier for the metric.value
(Any): The value of the metric.
get_metric¶
Retrieves a metric with the corresponding resource ID and metric ID.
def get_metric(self, resource, id, *args, **kwargs)
resource
(object): A resource object.id
(str): The metric ID.
Returns:
- The metric value.
log_metrics¶
Logs multiple metrics with the corresponding resource ID.
def log_metrics(self, resource, metrics, *args, **kwargs)
Arguments:
resource
(object): A resource object.metrics
(dict): A dictionary containing the metrics to log.
copy_metrics¶
Copies metrics from one resource to another.
de copy_metrics(self, from_resource, to_resource, *args, **kwargs)
Arguments:
from_resource
(object): The resource to copy metrics from.to_resource
(object): The resource to copy metrics to.
log_prediction_metrics¶
Logs prediction metrics with the corresponding prediction job ID and list of predictions.
def log_prediction_metrics(self, prediction_job, predictions, *args, **kwargs)
Arguments:
prediction_job
(object): A prediction job object.predictions
(object): The predictions.