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new 7101e88201d [SPARK-37405][FOLLOW-UP][PYTHON][ML] Move _input_kwargs
hints to consistent positions
7101e88201d is described below
commit 7101e88201d88cf24057187f360c828d1b376589
Author: zero323 <mszymkiew...@gmail.com>
AuthorDate: Fri Apr 15 10:32:13 2022 +0200
[SPARK-37405][FOLLOW-UP][PYTHON][ML] Move _input_kwargs hints to consistent
positions
### What changes were proposed in this pull request?
This PR moves `_input_kwargs` hints to beginning of the bodies of the
annotated classes.
### Why are the changes needed?
Consistency with other modules.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes #36203 from zero323/SPARK-37405-FOLLOW-UP.
Authored-by: zero323 <mszymkiew...@gmail.com>
Signed-off-by: zero323 <mszymkiew...@gmail.com>
(cherry picked from commit 797abc069348a2770742d5b57fd8c0fab0abe8d4)
Signed-off-by: zero323 <mszymkiew...@gmail.com>
---
python/pyspark/ml/feature.py | 68 ++++++++++++++++++++++----------------------
1 file changed, 34 insertions(+), 34 deletions(-)
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 8cebea2363d..7136c29f156 100755
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -177,6 +177,8 @@ class Binarizer(
...
"""
+ _input_kwargs: Dict[str, Any]
+
threshold: Param[float] = Param(
Params._dummy(),
"threshold",
@@ -195,8 +197,6 @@ class Binarizer(
typeConverter=TypeConverters.toListFloat,
)
- _input_kwargs: Dict[str, Any]
-
@overload
def __init__(
self,
@@ -721,6 +721,8 @@ class Bucketizer(
...
"""
+ _input_kwargs: Dict[str, Any]
+
splits: Param[List[float]] = Param(
Params._dummy(),
"splits",
@@ -762,8 +764,6 @@ class Bucketizer(
typeConverter=TypeConverters.toListListFloat,
)
- _input_kwargs: Dict[str, Any]
-
@overload
def __init__(
self,
@@ -1284,6 +1284,8 @@ class DCT(JavaTransformer, HasInputCol, HasOutputCol,
JavaMLReadable["DCT"], Jav
False
"""
+ _input_kwargs: Dict[str, Any]
+
inverse: Param[bool] = Param(
Params._dummy(),
"inverse",
@@ -1291,8 +1293,6 @@ class DCT(JavaTransformer, HasInputCol, HasOutputCol,
JavaMLReadable["DCT"], Jav
typeConverter=TypeConverters.toBoolean,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self,
@@ -1392,6 +1392,8 @@ class ElementwiseProduct(
True
"""
+ _input_kwargs: Dict[str, Any]
+
scalingVec: Param[Vector] = Param(
Params._dummy(),
"scalingVec",
@@ -1399,8 +1401,6 @@ class ElementwiseProduct(
typeConverter=TypeConverters.toVector,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self,
@@ -1528,6 +1528,8 @@ class FeatureHasher(
True
"""
+ _input_kwargs: Dict[str, Any]
+
categoricalCols: Param[List[str]] = Param(
Params._dummy(),
"categoricalCols",
@@ -1535,8 +1537,6 @@ class FeatureHasher(
typeConverter=TypeConverters.toListString,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self,
@@ -1650,6 +1650,8 @@ class HashingTF(
5
"""
+ _input_kwargs: Dict[str, Any]
+
binary: Param[bool] = Param(
Params._dummy(),
"binary",
@@ -1659,8 +1661,6 @@ class HashingTF(
typeConverter=TypeConverters.toBoolean,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self,
@@ -2882,6 +2882,8 @@ class NGram(JavaTransformer, HasInputCol, HasOutputCol,
JavaMLReadable["NGram"],
True
"""
+ _input_kwargs: Dict[str, Any]
+
n: Param[int] = Param(
Params._dummy(),
"n",
@@ -2889,8 +2891,6 @@ class NGram(JavaTransformer, HasInputCol, HasOutputCol,
JavaMLReadable["NGram"],
typeConverter=TypeConverters.toInt,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self, *, n: int = 2, inputCol: Optional[str] = None, outputCol:
Optional[str] = None
@@ -2982,10 +2982,10 @@ class Normalizer(
True
"""
- p = Param(Params._dummy(), "p", "the p norm value.",
typeConverter=TypeConverters.toFloat)
-
_input_kwargs: Dict[str, Any]
+ p = Param(Params._dummy(), "p", "the p norm value.",
typeConverter=TypeConverters.toFloat)
+
@keyword_only
def __init__(
self, *, p: float = 2.0, inputCol: Optional[str] = None, outputCol:
Optional[str] = None
@@ -3378,6 +3378,8 @@ class PolynomialExpansion(
True
"""
+ _input_kwargs: Dict[str, Any]
+
degree: Param[int] = Param(
Params._dummy(),
"degree",
@@ -3385,8 +3387,6 @@ class PolynomialExpansion(
typeConverter=TypeConverters.toInt,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self, *, degree: int = 2, inputCol: Optional[str] = None, outputCol:
Optional[str] = None
@@ -3546,6 +3546,8 @@ class QuantileDiscretizer(
...
"""
+ _input_kwargs: Dict[str, Any]
+
numBuckets: Param[int] = Param(
Params._dummy(),
"numBuckets",
@@ -3579,8 +3581,6 @@ class QuantileDiscretizer(
typeConverter=TypeConverters.toListInt,
)
- _input_kwargs: Dict[str, Any]
-
@overload
def __init__(
self,
@@ -4076,6 +4076,8 @@ class RegexTokenizer(
True
"""
+ _input_kwargs: Dict[str, Any]
+
minTokenLength: Param[int] = Param(
Params._dummy(),
"minTokenLength",
@@ -4100,8 +4102,6 @@ class RegexTokenizer(
typeConverter=TypeConverters.toBoolean,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self,
@@ -4237,12 +4237,12 @@ class SQLTransformer(JavaTransformer,
JavaMLReadable["SQLTransformer"], JavaMLWr
True
"""
+ _input_kwargs: Dict[str, Any]
+
statement = Param(
Params._dummy(), "statement", "SQL statement",
typeConverter=TypeConverters.toString
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(self, *, statement: Optional[str] = None):
"""
@@ -4874,6 +4874,8 @@ class IndexToString(
StringIndexer : for converting categorical values into category indices
"""
+ _input_kwargs: Dict[str, Any]
+
labels: Param[List[str]] = Param(
Params._dummy(),
"labels",
@@ -4882,8 +4884,6 @@ class IndexToString(
typeConverter=TypeConverters.toListString,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self,
@@ -4996,6 +4996,8 @@ class StopWordsRemover(
...
"""
+ _input_kwargs: Dict[str, Any]
+
stopWords: Param[List[str]] = Param(
Params._dummy(),
"stopWords",
@@ -5015,8 +5017,6 @@ class StopWordsRemover(
typeConverter=TypeConverters.toString,
)
- _input_kwargs: Dict[str, Any]
-
@overload
def __init__(
self,
@@ -5327,6 +5327,8 @@ class VectorAssembler(
...
"""
+ _input_kwargs: Dict[str, Any]
+
handleInvalid: Param[str] = Param(
Params._dummy(),
"handleInvalid",
@@ -5341,8 +5343,6 @@ class VectorAssembler(
typeConverter=TypeConverters.toString,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self,
@@ -5690,6 +5690,8 @@ class VectorSlicer(
True
"""
+ _input_kwargs: Dict[str, Any]
+
indices: Param[List[int]] = Param(
Params._dummy(),
"indices",
@@ -5707,8 +5709,6 @@ class VectorSlicer(
typeConverter=TypeConverters.toListString,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self,
@@ -6909,6 +6909,8 @@ class VectorSizeHint(
True
"""
+ _input_kwargs: Dict[str, Any]
+
size: Param[int] = Param(
Params._dummy(), "size", "Size of vectors in column.",
typeConverter=TypeConverters.toInt
)
@@ -6924,8 +6926,6 @@ class VectorSizeHint(
TypeConverters.toString,
)
- _input_kwargs: Dict[str, Any]
-
@keyword_only
def __init__(
self,
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