diff options
Diffstat (limited to 'examples/src/main/python/ml/pipeline_example.py')
| -rw-r--r-- | examples/src/main/python/ml/pipeline_example.py | 10 |
1 files changed, 6 insertions, 4 deletions
diff --git a/examples/src/main/python/ml/pipeline_example.py b/examples/src/main/python/ml/pipeline_example.py index bd10cfd7a2..2d0865578a 100644 --- a/examples/src/main/python/ml/pipeline_example.py +++ b/examples/src/main/python/ml/pipeline_example.py @@ -38,12 +38,13 @@ if __name__ == "__main__": (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"]) + (3L, "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") hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features") - lr = LogisticRegression(maxIter=10, regParam=0.01) + lr = LogisticRegression(maxIter=10, regParam=0.001) pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) # Fit the pipeline to training documents. @@ -53,8 +54,9 @@ if __name__ == "__main__": test = spark.createDataFrame([ (4L, "spark i j k"), (5L, "l m n"), - (6L, "mapreduce spark"), - (7L, "apache hadoop")], ["id", "text"]) + (6L, "spark hadoop spark"), + (7L, "apache hadoop") + ], ["id", "text"]) # Make predictions on test documents and print columns of interest. prediction = model.transform(test) |