diff options
author | hyukjinkwon <gurwls223@gmail.com> | 2016-05-19 08:52:41 +0200 |
---|---|---|
committer | Nick Pentreath <nickp@za.ibm.com> | 2016-05-19 08:52:41 +0200 |
commit | e2ec32dab8530aa21ec95a27d60b1c22f3d1a18c (patch) | |
tree | 6c6f9fa78578b1a23f954c27beeb509c203098b0 /examples/src/main/python/ml | |
parent | 661c21049b62ebfaf788dcbc31d62a09e206265b (diff) | |
download | spark-e2ec32dab8530aa21ec95a27d60b1c22f3d1a18c.tar.gz spark-e2ec32dab8530aa21ec95a27d60b1c22f3d1a18c.tar.bz2 spark-e2ec32dab8530aa21ec95a27d60b1c22f3d1a18c.zip |
[SPARK-15031][EXAMPLES][FOLLOW-UP] Make Python param example working with SparkSession
## What changes were proposed in this pull request?
It seems most of Python examples were changed to use SparkSession by https://github.com/apache/spark/pull/12809. This PR said both examples below:
- `simple_params_example.py`
- `aft_survival_regression.py`
are not changed because it dose not work. It seems `aft_survival_regression.py` is changed by https://github.com/apache/spark/pull/13050 but `simple_params_example.py` is not yet.
This PR corrects the example and make this use SparkSession.
In more detail, it seems `threshold` is replaced to `thresholds` here and there by https://github.com/apache/spark/commit/5a23213c148bfe362514f9c71f5273ebda0a848a. However, when it calls `lr.fit(training, paramMap)` this overwrites the values. So, `threshold` was 5 and `thresholds` becomes 5.5 (by `1 / (1 + thresholds(0) / thresholds(1)`).
According to the comment below. this is not allowed, https://github.com/apache/spark/blob/354f8f11bd4b20fa99bd67a98da3525fd3d75c81/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala#L58-L61.
So, in this PR, it sets the equivalent value so that this does not throw an exception.
## How was this patch tested?
Manully (`mvn package -DskipTests && spark-submit simple_params_example.py`)
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #13135 from HyukjinKwon/SPARK-15031.
Diffstat (limited to 'examples/src/main/python/ml')
-rw-r--r-- | examples/src/main/python/ml/simple_params_example.py | 24 |
1 files changed, 11 insertions, 13 deletions
diff --git a/examples/src/main/python/ml/simple_params_example.py b/examples/src/main/python/ml/simple_params_example.py index 2d6d115d54..c57e59d01b 100644 --- a/examples/src/main/python/ml/simple_params_example.py +++ b/examples/src/main/python/ml/simple_params_example.py @@ -20,11 +20,10 @@ from __future__ import print_function import pprint import sys -from pyspark import SparkContext from pyspark.ml.classification import LogisticRegression from pyspark.mllib.linalg import DenseVector from pyspark.mllib.regression import LabeledPoint -from pyspark.sql import SQLContext +from pyspark.sql import SparkSession """ A simple example demonstrating ways to specify parameters for Estimators and Transformers. @@ -33,21 +32,20 @@ Run with: """ if __name__ == "__main__": - if len(sys.argv) > 1: - print("Usage: simple_params_example", file=sys.stderr) - exit(1) - sc = SparkContext(appName="PythonSimpleParamsExample") - sqlContext = SQLContext(sc) + spark = SparkSession \ + .builder \ + .appName("SimpleTextClassificationPipeline") \ + .getOrCreate() # prepare training data. # We create an RDD of LabeledPoints and convert them into a DataFrame. # A LabeledPoint is an Object with two fields named label and features # and Spark SQL identifies these fields and creates the schema appropriately. - training = sc.parallelize([ + training = spark.createDataFrame([ LabeledPoint(1.0, DenseVector([0.0, 1.1, 0.1])), LabeledPoint(0.0, DenseVector([2.0, 1.0, -1.0])), LabeledPoint(0.0, DenseVector([2.0, 1.3, 1.0])), - LabeledPoint(1.0, DenseVector([0.0, 1.2, -0.5]))]).toDF() + LabeledPoint(1.0, DenseVector([0.0, 1.2, -0.5]))]) # Create a LogisticRegression instance with maxIter = 10. # This instance is an Estimator. @@ -70,7 +68,7 @@ if __name__ == "__main__": # We may alternatively specify parameters using a parameter map. # paramMap overrides all lr parameters set earlier. - paramMap = {lr.maxIter: 20, lr.thresholds: [0.45, 0.55], lr.probabilityCol: "myProbability"} + paramMap = {lr.maxIter: 20, lr.thresholds: [0.5, 0.5], lr.probabilityCol: "myProbability"} # Now learn a new model using the new parameters. model2 = lr.fit(training, paramMap) @@ -78,10 +76,10 @@ if __name__ == "__main__": pprint.pprint(model2.extractParamMap()) # prepare test data. - test = sc.parallelize([ + test = spark.createDataFrame([ LabeledPoint(1.0, DenseVector([-1.0, 1.5, 1.3])), LabeledPoint(0.0, DenseVector([3.0, 2.0, -0.1])), - LabeledPoint(0.0, DenseVector([0.0, 2.2, -1.5]))]).toDF() + LabeledPoint(0.0, DenseVector([0.0, 2.2, -1.5]))]) # Make predictions on test data using the Transformer.transform() method. # LogisticRegressionModel.transform will only use the 'features' column. @@ -95,4 +93,4 @@ if __name__ == "__main__": print("features=%s,label=%s -> prob=%s, prediction=%s" % (row.features, row.label, row.myProbability, row.prediction)) - sc.stop() + spark.stop() |