diff options
Diffstat (limited to 'examples/src/main/python/ml/simple_params_example.py')
| -rw-r--r-- | examples/src/main/python/ml/simple_params_example.py | 19 |
1 files changed, 9 insertions, 10 deletions
diff --git a/examples/src/main/python/ml/simple_params_example.py b/examples/src/main/python/ml/simple_params_example.py index c57e59d01b..54fbc2c9d0 100644 --- a/examples/src/main/python/ml/simple_params_example.py +++ b/examples/src/main/python/ml/simple_params_example.py @@ -21,9 +21,8 @@ import pprint import sys from pyspark.ml.classification import LogisticRegression -from pyspark.mllib.linalg import DenseVector -from pyspark.mllib.regression import LabeledPoint -from pyspark.sql import SparkSession +from pyspark.ml.linalg import DenseVector +from pyspark.sql import Row, SparkSession """ A simple example demonstrating ways to specify parameters for Estimators and Transformers. @@ -42,10 +41,10 @@ if __name__ == "__main__": # A LabeledPoint is an Object with two fields named label and features # and Spark SQL identifies these fields and creates the schema appropriately. 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]))]) + Row(label=1.0, features=DenseVector([0.0, 1.1, 0.1])), + Row(label=0.0, features=DenseVector([2.0, 1.0, -1.0])), + Row(label=0.0, features=DenseVector([2.0, 1.3, 1.0])), + Row(label=1.0, features=DenseVector([0.0, 1.2, -0.5]))]) # Create a LogisticRegression instance with maxIter = 10. # This instance is an Estimator. @@ -77,9 +76,9 @@ if __name__ == "__main__": # prepare test data. 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]))]) + Row(label=1.0, features=DenseVector([-1.0, 1.5, 1.3])), + Row(label=0.0, features=DenseVector([3.0, 2.0, -0.1])), + Row(label=0.0, features=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. |