diff options
| -rw-r--r-- | python/pyspark/ml/clustering.py | 3 | ||||
| -rw-r--r-- | python/pyspark/ml/feature.py | 17 | ||||
| -rw-r--r-- | python/pyspark/ml/recommendation.py | 12 |
3 files changed, 19 insertions, 13 deletions
diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py index 64c4bf1b92..05aa2dfe74 100644 --- a/python/pyspark/ml/clustering.py +++ b/python/pyspark/ml/clustering.py @@ -92,7 +92,8 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol initMode = Param(Params._dummy(), "initMode", "the initialization algorithm. This can be either \"random\" to " + "choose random points as initial cluster centers, or \"k-means||\" " + - "to use a parallel variant of k-means++", TypeConverters.toString) + "to use a parallel variant of k-means++", + typeConverter=TypeConverters.toString) initSteps = Param(Params._dummy(), "initSteps", "steps for k-means initialization mode", typeConverter=TypeConverters.toInt) diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index 49a78ede37..4310f154b5 100644 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -1317,9 +1317,9 @@ class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, typeConverter=TypeConverters.toInt) gaps = Param(Params._dummy(), "gaps", "whether regex splits on gaps (True) or matches tokens") pattern = Param(Params._dummy(), "pattern", "regex pattern (Java dialect) used for tokenizing", - TypeConverters.toString) + typeConverter=TypeConverters.toString) toLowercase = Param(Params._dummy(), "toLowercase", "whether to convert all characters to " + - "lowercase before tokenizing", TypeConverters.toBoolean) + "lowercase before tokenizing", typeConverter=TypeConverters.toBoolean) @keyword_only def __init__(self, minTokenLength=1, gaps=True, pattern="\\s+", inputCol=None, @@ -1430,7 +1430,8 @@ class SQLTransformer(JavaTransformer, JavaMLReadable, JavaMLWritable): .. versionadded:: 1.6.0 """ - statement = Param(Params._dummy(), "statement", "SQL statement", TypeConverters.toString) + statement = Param(Params._dummy(), "statement", "SQL statement", + typeConverter=TypeConverters.toString) @keyword_only def __init__(self, statement=None): @@ -1504,9 +1505,10 @@ class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, J .. versionadded:: 1.4.0 """ - withMean = Param(Params._dummy(), "withMean", "Center data with mean", TypeConverters.toBoolean) + withMean = Param(Params._dummy(), "withMean", "Center data with mean", + typeConverter=TypeConverters.toBoolean) withStd = Param(Params._dummy(), "withStd", "Scale to unit standard deviation", - TypeConverters.toBoolean) + typeConverter=TypeConverters.toBoolean) @keyword_only def __init__(self, withMean=False, withStd=True, inputCol=None, outputCol=None): @@ -1754,7 +1756,7 @@ class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadabl stopWords = Param(Params._dummy(), "stopWords", "The words to be filtered out", typeConverter=TypeConverters.toListString) caseSensitive = Param(Params._dummy(), "caseSensitive", "whether to do a case sensitive " + - "comparison over the stop words", TypeConverters.toBoolean) + "comparison over the stop words", typeConverter=TypeConverters.toBoolean) @keyword_only def __init__(self, inputCol=None, outputCol=None, stopWords=None, @@ -2510,7 +2512,8 @@ class RFormula(JavaEstimator, HasFeaturesCol, HasLabelCol, JavaMLReadable, JavaM .. versionadded:: 1.5.0 """ - formula = Param(Params._dummy(), "formula", "R model formula", TypeConverters.toString) + formula = Param(Params._dummy(), "formula", "R model formula", + typeConverter=TypeConverters.toString) @keyword_only def __init__(self, formula=None, featuresCol="features", labelCol="label"): diff --git a/python/pyspark/ml/recommendation.py b/python/pyspark/ml/recommendation.py index 9c38f2431b..9d7f22a66f 100644 --- a/python/pyspark/ml/recommendation.py +++ b/python/pyspark/ml/recommendation.py @@ -107,16 +107,18 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha numItemBlocks = Param(Params._dummy(), "numItemBlocks", "number of item blocks", typeConverter=TypeConverters.toInt) implicitPrefs = Param(Params._dummy(), "implicitPrefs", "whether to use implicit preference", - TypeConverters.toBoolean) + typeConverter=TypeConverters.toBoolean) alpha = Param(Params._dummy(), "alpha", "alpha for implicit preference", typeConverter=TypeConverters.toFloat) - userCol = Param(Params._dummy(), "userCol", "column name for user ids", TypeConverters.toString) - itemCol = Param(Params._dummy(), "itemCol", "column name for item ids", TypeConverters.toString) + userCol = Param(Params._dummy(), "userCol", "column name for user ids", + typeConverter=TypeConverters.toString) + itemCol = Param(Params._dummy(), "itemCol", "column name for item ids", + typeConverter=TypeConverters.toString) ratingCol = Param(Params._dummy(), "ratingCol", "column name for ratings", - TypeConverters.toString) + typeConverter=TypeConverters.toString) nonnegative = Param(Params._dummy(), "nonnegative", "whether to use nonnegative constraint for least squares", - TypeConverters.toBoolean) + typeConverter=TypeConverters.toBoolean) @keyword_only def __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, |