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new 4399755 [SPARK-38243][PYTHON][ML] Fix
pyspark.ml.LogisticRegression.getThreshold error message logic
4399755 is described below
commit 439975590cf4f21c2a548a2ac6231eb234e1a2f3
Author: zero323 <mszymkiew...@gmail.com>
AuthorDate: Fri Feb 18 11:08:33 2022 -0600
[SPARK-38243][PYTHON][ML] Fix pyspark.ml.LogisticRegression.getThreshold
error message logic
### What changes were proposed in this pull request?
This PR replaces incorrect usage of `str.join` on a `List[float]` in
`LogisticRegression.getThreshold`.
### Why are the changes needed?
To avoid unexpected failure if method is used in case of multi-class
classification.
After this change, the following code:
```python
from pyspark.ml.classification import LogisticRegression
LogisticRegression(thresholds=[1.0, 2.0, 3.0]).getThreshold()
```
raises
```python
Traceback (most recent call last):
Input In [4] in <module>
model.getThreshold()
File /path/to/spark/python/pyspark/ml/classification.py:999 in
getThreshold
raise ValueError(
ValueError: Logistic Regression getThreshold only applies to binary
classification, but thresholds has length != 2. thresholds: [1.0, 2.0, 3.0]
```
instead of current
```python
Traceback (most recent call last):
Input In [7] in <module>
model.getThreshold()
File /path/to/spark/python/pyspark/ml/classification.py:1003 in
getThreshold
+ ",".join(ts)
TypeError: sequence item 0: expected str instance, float found
```
### Does this PR introduce _any_ user-facing change?
No. Bugfix.
### How was this patch tested?
Manual testing.
Closes #35558 from zero323/SPARK-38243.
Authored-by: zero323 <mszymkiew...@gmail.com>
Signed-off-by: Sean Owen <sro...@gmail.com>
---
python/pyspark/ml/classification.py | 3 +--
1 file changed, 1 insertion(+), 2 deletions(-)
diff --git a/python/pyspark/ml/classification.py
b/python/pyspark/ml/classification.py
index 058740e..b791e6f 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -999,8 +999,7 @@ class _LogisticRegressionParams(
raise ValueError(
"Logistic Regression getThreshold only applies to"
+ " binary classification, but thresholds has length != 2."
- + " thresholds: "
- + ",".join(ts)
+ + " thresholds: {ts}".format(ts=ts)
)
return 1.0 / (1.0 + ts[0] / ts[1])
else:
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