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Merge pull request #158 from jkasiraj/tracker
doc: view ROC, PR, and confusion matrix in Trial Component UI
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src/smexperiments/tracker.py

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@@ -478,9 +478,9 @@ def log_precision_recall(
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output_artifact=True,
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no_skill=None,
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):
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"""Log a precision recall graph artifact. If your job is created by a pipeline
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execution you can view the artifact by selecting the corresponding step in the pipelines UI.
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The Trial Component UI currently does not support rendering of the artifact.
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"""Log a precision recall graph artifact. You can view the artifact in the charts tab of the
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Trial Component UI. If your job is created by a pipeline execution you can view the artifact
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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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Requires sklearn.
@@ -542,9 +542,9 @@ def log_roc_curve(
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title=None,
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output_artifact=True,
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):
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"""Log a receiver operating characteristic (ROC curve) artifact. If your job is created by a pipeline
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execution you can view the artifact by selecting the corresponding step in the pipelines UI.
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The Trial Component UI currently does not support rendering of the artifact.
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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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Requires sklearn.
@@ -595,9 +595,9 @@ def log_confusion_matrix(
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title=None,
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output_artifact=True,
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):
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"""Log a confusion matrix artifact. If your job is created by a pipeline execution you can view the
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artifact by selecting the corresponding step in the pipelines UI. The Trial Component UI
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currently does not support rendering of the artifact.
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"""Log a confusion matrix artifact. You can view the artifact in the charts tab of the
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Trial Component UI. If your job is created by a pipeline execution you can view the
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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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Requires sklearn.

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