[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.

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()