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authorZheng RuiFeng <ruifengz@foxmail.com>2016-06-06 09:35:47 +0100
committerSean Owen <sowen@cloudera.com>2016-06-06 09:35:47 +0100
commitfd8af397132fa1415a4c19d7f5cb5a41aa6ddb27 (patch)
treea653b3542d0671c8cb8b3ff7fa3755525c0606a4 /python/pyspark/ml
parent32f2f95dbdfb21491e46d4b608fd4e8ac7ab8973 (diff)
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[MINOR] Fix Typos 'an -> a'
## What changes were proposed in this pull request? `an -> a` Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one. ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13515 from zhengruifeng/an_a.
Diffstat (limited to 'python/pyspark/ml')
-rw-r--r--python/pyspark/ml/classification.py4
-rw-r--r--python/pyspark/ml/pipeline.py2
2 files changed, 3 insertions, 3 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 7710fdf2e2..77badebeb4 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -351,7 +351,7 @@ class BinaryLogisticRegressionSummary(LogisticRegressionSummary):
def roc(self):
"""
Returns the receiver operating characteristic (ROC) curve,
- which is an Dataframe having two fields (FPR, TPR) with
+ which is a Dataframe having two fields (FPR, TPR) with
(0.0, 0.0) prepended and (1.0, 1.0) appended to it.
.. seealso:: `Wikipedia reference \
@@ -380,7 +380,7 @@ class BinaryLogisticRegressionSummary(LogisticRegressionSummary):
@since("2.0.0")
def pr(self):
"""
- Returns the precision-recall curve, which is an Dataframe
+ Returns the precision-recall curve, which is a Dataframe
containing two fields recall, precision with (0.0, 1.0) prepended
to it.
diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py
index eb6d3310d6..0777527134 100644
--- a/python/pyspark/ml/pipeline.py
+++ b/python/pyspark/ml/pipeline.py
@@ -42,7 +42,7 @@ class Pipeline(Estimator, MLReadable, MLWritable):
stage. If a stage is a :py:class:`Transformer`, its
:py:meth:`Transformer.transform` method will be called to produce
the dataset for the next stage. The fitted model from a
- :py:class:`Pipeline` is an :py:class:`PipelineModel`, which
+ :py:class:`Pipeline` is a :py:class:`PipelineModel`, which
consists of fitted models and transformers, corresponding to the
pipeline stages. If there are no stages, the pipeline acts as an
identity transformer.