diff --git a/src/smexperiments/tracker.py b/src/smexperiments/tracker.py
index f8cc723..0bde5f7 100644
--- a/src/smexperiments/tracker.py
+++ b/src/smexperiments/tracker.py
@@ -52,7 +52,7 @@ class Tracker(object):
Note that parameters and input/output artifacts are saved to SageMaker directly via the
UpdateTrialComponent operation. In contrast metrics (via `log_metric` method) are saved to a file, which is
- then ingested into SageMaker via a metrics agent _which only runs on training job hosts. As a result any metrics
+ then ingested into SageMaker via a metrics agent which only runs on training job hosts. As a result any metrics
logged in non-training job host environments will not be ingested into SageMaker.
Parameters:
@@ -495,7 +495,7 @@ def log_precision_recall(
y_scores = [0.1, 0.4, 0.35, 0.8]
no_skill = len(y_true[y_true==1]) / len(y_true)
- my_tracker._log_precision_recall(y_true, y_scores, no_skill=no_skill)
+ my_tracker.log_precision_recall(y_true, y_scores, no_skill=no_skill)
Args:
y_true (array): True labels. If labels are not binary then positive_label should be given.
@@ -548,7 +548,7 @@ def log_roc_curve(
"""Log a receiver operating characteristic (ROC curve) artifact. You can view the artifact
in the charts tab of the Trial Component UI. If your job is created by a pipeline execution
you can view the artifact by selecting the corresponding step in the pipelines UI.
- See also `SageMaker Pipelines `_
+ See also `SageMaker Pipelines `.
Requires sklearn.
@@ -615,7 +615,7 @@ def log_confusion_matrix(
Args:
- y_true (array): True labels. If labels are not binary then positive_label should be given.
+ y_true (array): True labels.
y_pred (array): Predicted labels.
title (str, optional): Title of the graph, Defaults to none.
output_artifact (boolean, optional): Determines if the artifact is associated with the