diff options
Diffstat (limited to 'python/pyspark/ml/recommendation.py')
| -rw-r--r-- | python/pyspark/ml/recommendation.py | 25 |
1 files changed, 16 insertions, 9 deletions
diff --git a/python/pyspark/ml/recommendation.py b/python/pyspark/ml/recommendation.py index de4c2675ed..7c7a1b67a1 100644 --- a/python/pyspark/ml/recommendation.py +++ b/python/pyspark/ml/recommendation.py @@ -100,16 +100,23 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha .. versionadded:: 1.4.0 """ - rank = Param(Params._dummy(), "rank", "rank of the factorization") - numUserBlocks = Param(Params._dummy(), "numUserBlocks", "number of user blocks") - numItemBlocks = Param(Params._dummy(), "numItemBlocks", "number of item blocks") - implicitPrefs = Param(Params._dummy(), "implicitPrefs", "whether to use implicit preference") - alpha = Param(Params._dummy(), "alpha", "alpha for implicit preference") - userCol = Param(Params._dummy(), "userCol", "column name for user ids") - itemCol = Param(Params._dummy(), "itemCol", "column name for item ids") - ratingCol = Param(Params._dummy(), "ratingCol", "column name for ratings") + rank = Param(Params._dummy(), "rank", "rank of the factorization", + typeConverter=TypeConverters.toInt) + numUserBlocks = Param(Params._dummy(), "numUserBlocks", "number of user blocks", + typeConverter=TypeConverters.toInt) + numItemBlocks = Param(Params._dummy(), "numItemBlocks", "number of item blocks", + typeConverter=TypeConverters.toInt) + implicitPrefs = Param(Params._dummy(), "implicitPrefs", "whether to use implicit preference", + 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) + ratingCol = Param(Params._dummy(), "ratingCol", "column name for ratings", + TypeConverters.toString) nonnegative = Param(Params._dummy(), "nonnegative", - "whether to use nonnegative constraint for least squares") + "whether to use nonnegative constraint for least squares", + TypeConverters.toBoolean) @keyword_only def __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, |