@@ -52,7 +52,7 @@ class Tracker(object):
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Note that parameters and input/output artifacts are saved to SageMaker directly via the
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UpdateTrialComponent operation. In contrast metrics (via `log_metric` method) are saved to a file, which is
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- then ingested into SageMaker via a metrics agent _which only runs on training job hosts. As a result any metrics
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+ then ingested into SageMaker via a metrics agent which only runs on training job hosts. As a result any metrics
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logged in non-training job host environments will not be ingested into SageMaker.
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Parameters:
@@ -495,7 +495,7 @@ def log_precision_recall(
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y_scores = [0.1, 0.4, 0.35, 0.8]
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no_skill = len(y_true[y_true==1]) / len(y_true)
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- my_tracker._log_precision_recall (y_true, y_scores, no_skill=no_skill)
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+ my_tracker.log_precision_recall (y_true, y_scores, no_skill=no_skill)
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Args:
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y_true (array): True labels. If labels are not binary then positive_label should be given.
@@ -548,7 +548,7 @@ def log_roc_curve(
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"""Log a receiver operating characteristic (ROC curve) artifact. You can view the artifact
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in the charts tab of the Trial Component UI. If your job is created by a pipeline execution
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you can view the artifact by selecting the corresponding step in the pipelines UI.
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- See also `SageMaker Pipelines <https://aws.amazon.com/sagemaker/pipelines/>`_
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+ See also `SageMaker Pipelines <https://aws.amazon.com/sagemaker/pipelines/>`.
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Requires sklearn.
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@@ -615,7 +615,7 @@ def log_confusion_matrix(
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Args:
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- y_true (array): True labels. If labels are not binary then positive_label should be given.
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+ y_true (array): True labels.
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y_pred (array): Predicted labels.
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title (str, optional): Title of the graph, Defaults to none.
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output_artifact (boolean, optional): Determines if the artifact is associated with the
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