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| author | DB Tsai <dbt@netflix.com> | 2016-05-17 12:51:07 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2016-05-17 12:51:07 -0700 |
| commit | e2efe0529acd748f26dbaa41331d1733ed256237 (patch) | |
| tree | fe1a5aeeadfbf220b5dbe1429e0235153db8117b /python/pyspark/ml/tuning.py | |
| parent | 9f176dd3918129a72282a6b7a12e2899cbb6dac9 (diff) | |
| download | spark-e2efe0529acd748f26dbaa41331d1733ed256237.tar.gz spark-e2efe0529acd748f26dbaa41331d1733ed256237.tar.bz2 spark-e2efe0529acd748f26dbaa41331d1733ed256237.zip | |
[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 <dbt@netflix.com>
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes #12627 from dbtsai/SPARK-14615-NewML.
Diffstat (limited to 'python/pyspark/ml/tuning.py')
| -rw-r--r-- | python/pyspark/ml/tuning.py | 4 |
1 files changed, 2 insertions, 2 deletions
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), |