qq=vat.transform(dat).head() qq qq = poly.transform(df).head() >>> px = PolynomialExpansion(degree=2, inputCol="dense", outputCol="expanded") >>> px.transform(df).head().expanded DenseVector([0.5, 0.25, 2.0, 1.0, 4.0]) >>> px.setParams(outputCol="test").transform(df).head().test DenseVector([0.5, 0.25, 2.0, 1.0, 4.0]) >>> polyExpansionPath = temp_path + "/poly-expansion" >>> px.save(polyExpansionPath) >>> loadedPx = PolynomialExpansion.load(polyExpansionPath) >>> loadedPx.getDegree() == px.getDegree() True from pyspark.ml.classification import LogisticRegression from pyspark.ml.feature import HashingTF, Tokenizer # Prepare training documents from a list of (id, text, label) tuples. training = spark.createDataFrame([ (0, "a b c d e spark", 1.0), (1, "b d", 0.0), (2, "spark f g h", 1.0),