Getting labels from StringIndexer stages within pipeline in Spark (pyspark)

Question

I am using Spark and pyspark and I have a pipeline set up with a bunch of StringIndexer objects, that I use to encode the string columns to columns of indices:

indexers = [StringIndexer(inputCol=column, outputCol=column + '_index').setHandleInvalid('skip')
            for column in list(set(data_frame.columns) - ignore_columns)]
pipeline = Pipeline(stages=indexers)
new_data_frame = pipeline.fit(data_frame).transform(data_frame)

The problem is, that I need to get the list of labels for each StringIndexer object after it gets fitted. For a single column and a single StringIndexer without a pipeline, it's an easy task. I can just access the labels attribute after fitting the indexer on the DataFrame:

indexer = StringIndexer(inputCol="name", outputCol="name_index")
indexer_fitted = indexer.fit(data_frame)
labels = indexer_fitted.labels
new_data_frame = indexer_fitted.transform(data_frame)

However when I use the pipeline, this doesn't seem possible, or at least I don't know how to do this.

So I guess my question comes down to: Is there a way to access the labels that were used during the indexing process for each individual column?

Or will I have to ditch the pipeline in this use-case, and for example loop through the list of StringIndexer objects and do it manually? (I'm sure that would possible. However using the pipeline would just be a lot nicer)

Answer 1

Example data and Pipeline:

from pyspark.ml.feature import StringIndexer, StringIndexerModel

df = spark.createDataFrame([("a", "foo"), ("b", "bar")], ("x1", "x2"))

pipeline = Pipeline(stages=[
    StringIndexer(inputCol=c, outputCol='{}_index'.format(c))
    for c in df.columns
])

model = pipeline.fit(df)

Extract from stages:

# Accessing _java_obj shouldn't be necessary in Spark 2.3+
{x._java_obj.getOutputCol(): x.labels 
for x in model.stages if isinstance(x, StringIndexerModel)}

{'x1_index': ['a', 'b'], 'x2_index': ['foo', 'bar']}

From metadata of the transformed DataFrame:

indexed = model.transform(df)

{c.name: c.metadata["ml_attr"]["vals"]
for c in indexed.schema.fields if c.name.endswith("_index")}

{'x1_index': ['a', 'b'], 'x2_index': ['foo', 'bar']}