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Posted to commits@spark.apache.org by sr...@apache.org on 2016/11/02 09:23:39 UTC
spark git commit: [SPARK-18133][BRANCH-2.0][EXAMPLES][ML] Python ML Pipeline Exampl…
Repository: spark
Updated Branches:
refs/heads/branch-2.0 81f080425 -> 09178b6ee
[SPARK-18133][BRANCH-2.0][EXAMPLES][ML] Python ML Pipeline Exampl\u2026
## What changes were proposed in this pull request?
[Fix] [branch-2.0] In Python 3, there is only one integer type (i.e., int), which mostly behaves like the long type in Python 2. Since Python 3 won't accept "L", so removed "L" in all examples.
## How was this patch tested?
Unit tests.
\u2026e has syntax errors]
Author: Jagadeesan <as...@us.ibm.com>
Closes #15729 from jagadeesanas2/SPARK-18133_branch2.0.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/09178b6e
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/09178b6e
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/09178b6e
Branch: refs/heads/branch-2.0
Commit: 09178b6eefd33011c3e90164356a5d6c3ae737bd
Parents: 81f0804
Author: Jagadeesan <as...@us.ibm.com>
Authored: Wed Nov 2 09:23:30 2016 +0000
Committer: Sean Owen <so...@cloudera.com>
Committed: Wed Nov 2 09:23:30 2016 +0000
----------------------------------------------------------------------
examples/src/main/python/ml/cross_validator.py | 8 ++++----
examples/src/main/python/ml/pipeline_example.py | 16 ++++++++--------
.../mllib/binary_classification_metrics_example.py | 2 +-
.../python/mllib/multi_class_metrics_example.py | 2 +-
4 files changed, 14 insertions(+), 14 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/09178b6e/examples/src/main/python/ml/cross_validator.py
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diff --git a/examples/src/main/python/ml/cross_validator.py b/examples/src/main/python/ml/cross_validator.py
index a41df6c..283db5d 100644
--- a/examples/src/main/python/ml/cross_validator.py
+++ b/examples/src/main/python/ml/cross_validator.py
@@ -83,10 +83,10 @@ if __name__ == "__main__":
# Prepare test documents, which are unlabeled.
test = spark.createDataFrame([
- (4L, "spark i j k"),
- (5L, "l m n"),
- (6L, "mapreduce spark"),
- (7L, "apache hadoop")
+ (4, "spark i j k"),
+ (5, "l m n"),
+ (6, "mapreduce spark"),
+ (7, "apache hadoop")
], ["id", "text"])
# Make predictions on test documents. cvModel uses the best model found (lrModel).
http://git-wip-us.apache.org/repos/asf/spark/blob/09178b6e/examples/src/main/python/ml/pipeline_example.py
----------------------------------------------------------------------
diff --git a/examples/src/main/python/ml/pipeline_example.py b/examples/src/main/python/ml/pipeline_example.py
index bd10cfd..1926cd2 100644
--- a/examples/src/main/python/ml/pipeline_example.py
+++ b/examples/src/main/python/ml/pipeline_example.py
@@ -35,10 +35,10 @@ if __name__ == "__main__":
# $example on$
# Prepare training documents from a list of (id, text, label) tuples.
training = spark.createDataFrame([
- (0L, "a b c d e spark", 1.0),
- (1L, "b d", 0.0),
- (2L, "spark f g h", 1.0),
- (3L, "hadoop mapreduce", 0.0)], ["id", "text", "label"])
+ (0, "a b c d e spark", 1.0),
+ (1, "b d", 0.0),
+ (2, "spark f g h", 1.0),
+ (3, "hadoop mapreduce", 0.0)], ["id", "text", "label"])
# Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
tokenizer = Tokenizer(inputCol="text", outputCol="words")
@@ -51,10 +51,10 @@ if __name__ == "__main__":
# Prepare test documents, which are unlabeled (id, text) tuples.
test = spark.createDataFrame([
- (4L, "spark i j k"),
- (5L, "l m n"),
- (6L, "mapreduce spark"),
- (7L, "apache hadoop")], ["id", "text"])
+ (4, "spark i j k"),
+ (5, "l m n"),
+ (6, "mapreduce spark"),
+ (7, "apache hadoop")], ["id", "text"])
# Make predictions on test documents and print columns of interest.
prediction = model.transform(test)
http://git-wip-us.apache.org/repos/asf/spark/blob/09178b6e/examples/src/main/python/mllib/binary_classification_metrics_example.py
----------------------------------------------------------------------
diff --git a/examples/src/main/python/mllib/binary_classification_metrics_example.py b/examples/src/main/python/mllib/binary_classification_metrics_example.py
index daf000e..91f8378 100644
--- a/examples/src/main/python/mllib/binary_classification_metrics_example.py
+++ b/examples/src/main/python/mllib/binary_classification_metrics_example.py
@@ -39,7 +39,7 @@ if __name__ == "__main__":
.rdd.map(lambda row: LabeledPoint(row[0], row[1]))
# Split data into training (60%) and test (40%)
- training, test = data.randomSplit([0.6, 0.4], seed=11L)
+ training, test = data.randomSplit([0.6, 0.4], seed=11)
training.cache()
# Run training algorithm to build the model
http://git-wip-us.apache.org/repos/asf/spark/blob/09178b6e/examples/src/main/python/mllib/multi_class_metrics_example.py
----------------------------------------------------------------------
diff --git a/examples/src/main/python/mllib/multi_class_metrics_example.py b/examples/src/main/python/mllib/multi_class_metrics_example.py
index cd56b3c..7dc5fb4 100644
--- a/examples/src/main/python/mllib/multi_class_metrics_example.py
+++ b/examples/src/main/python/mllib/multi_class_metrics_example.py
@@ -32,7 +32,7 @@ if __name__ == "__main__":
data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt")
# Split data into training (60%) and test (40%)
- training, test = data.randomSplit([0.6, 0.4], seed=11L)
+ training, test = data.randomSplit([0.6, 0.4], seed=11)
training.cache()
# Run training algorithm to build the model
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