From e2efe0529acd748f26dbaa41331d1733ed256237 Mon Sep 17 00:00:00 2001 From: DB Tsai Date: Tue, 17 May 2016 12:51:07 -0700 Subject: [SPARK-14615][ML] Use the new ML Vector and Matrix in the ML pipeline based algorithms ## What changes were proposed in this pull request? Once SPARK-14487 and SPARK-14549 are merged, we will migrate to use the new vector and matrix type in the new ml pipeline based apis. ## How was this patch tested? Unit tests Author: DB Tsai Author: Liang-Chi Hsieh Author: Xiangrui Meng Closes #12627 from dbtsai/SPARK-14615-NewML. --- python/pyspark/ml/tuning.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'python/pyspark/ml/tuning.py') diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py index 0920ae6ea1..75789c4d09 100644 --- a/python/pyspark/ml/tuning.py +++ b/python/pyspark/ml/tuning.py @@ -151,7 +151,7 @@ class CrossValidator(Estimator, ValidatorParams): >>> from pyspark.ml.classification import LogisticRegression >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator - >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.linalg import Vectors >>> dataset = sqlContext.createDataFrame( ... [(Vectors.dense([0.0]), 0.0), ... (Vectors.dense([0.4]), 1.0), @@ -310,7 +310,7 @@ class TrainValidationSplit(Estimator, ValidatorParams): >>> from pyspark.ml.classification import LogisticRegression >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator - >>> from pyspark.mllib.linalg import Vectors + >>> from pyspark.ml.linalg import Vectors >>> dataset = sqlContext.createDataFrame( ... [(Vectors.dense([0.0]), 0.0), ... (Vectors.dense([0.4]), 1.0), -- cgit v1.2.3