diff options
Diffstat (limited to 'python/pyspark/ml/recommendation.py')
| -rw-r--r-- | python/pyspark/ml/recommendation.py | 11 |
1 files changed, 0 insertions, 11 deletions
diff --git a/python/pyspark/ml/recommendation.py b/python/pyspark/ml/recommendation.py index b44c66f73c..08180a2f25 100644 --- a/python/pyspark/ml/recommendation.py +++ b/python/pyspark/ml/recommendation.py @@ -85,7 +85,6 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha .. versionadded:: 1.4.0 """ - # a placeholder to make it appear in the generated doc 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") @@ -108,16 +107,6 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha """ super(ALS, self).__init__() self._java_obj = self._new_java_obj("org.apache.spark.ml.recommendation.ALS", self.uid) - self.rank = Param(self, "rank", "rank of the factorization") - self.numUserBlocks = Param(self, "numUserBlocks", "number of user blocks") - self.numItemBlocks = Param(self, "numItemBlocks", "number of item blocks") - self.implicitPrefs = Param(self, "implicitPrefs", "whether to use implicit preference") - self.alpha = Param(self, "alpha", "alpha for implicit preference") - self.userCol = Param(self, "userCol", "column name for user ids") - self.itemCol = Param(self, "itemCol", "column name for item ids") - self.ratingCol = Param(self, "ratingCol", "column name for ratings") - self.nonnegative = Param(self, "nonnegative", - "whether to use nonnegative constraint for least squares") self._setDefault(rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10, implicitPrefs=False, alpha=1.0, userCol="user", itemCol="item", seed=None, ratingCol="rating", nonnegative=False, checkpointInterval=10) |