title: PySpark Pipeline Error when using Indexer and Encoder
body: <p>I'm using the <a href="https://archive.ics.uci.edu/ml/datasets/bank+marketing" rel="nofollow noreferrer">bank data from UCI</a> to just template out a project. I was following the PySpark tutorial on on their documentation site (sorry cant find the link anymore). I keep getting an error when running through the pipeline. I've loaded the data, converted feature types, and have done the pipelining for categorical and numerical features. I'd love any feedback on any part of the code but specifically where I'm getting the error so I can continue with this build out. Thank you in advance!</p>
  
  <h1>Sample Data</h1>
  
  <pre><code>+---+---+----------+-------+---------+-------+-------+-------+----+-------+---+-----+--------+--------+-----+--------+--------+-------+
  | id|age|       job|marital|education|default|balance|housing|loan|contact|day|month|duration|campaign|pdays|previous|poutcome|deposit|
  +---+---+----------+-------+---------+-------+-------+-------+----+-------+---+-----+--------+--------+-----+--------+--------+-------+
  |  1| 59|    admin.|married|secondary|     no|   2343|    yes|  no|unknown|  5|  may|    1042|       1|   -1|       0| unknown|    yes|
  |  2| 56|    admin.|married|secondary|     no|     45|     no|  no|unknown|  5|  may|    1467|       1|   -1|       0| unknown|    yes|
  |  3| 41|technician|married|secondary|     no|   1270|    yes|  no|unknown|  5|  may|    1389|       1|   -1|       0| unknown|    yes|
  |  4| 55|  services|married|secondary|     no|   2476|    yes|  no|unknown|  5|  may|     579|       1|   -1|       0| unknown|    yes|
  |  5| 54|    admin.|married| tertiary|     no|    184|     no|  no|unknown|  5|  may|     673|       2|   -1|       0| unknown|    yes|
  +---+---+----------+-------+---------+-------+-------+-------+----+-------+---+-----+--------+--------+-----+--------+--------+-------+
  only showing top 5 rows
  </code></pre>
  
  
  
  <pre><code># Convert Feature Types
  df.createOrReplaceTempView("df")
  
  df2 = spark.sql("select \
                      cast(id as int) as id, \
                      cast(age as int) as age, \
                      cast(job as string) as job, \
                      cast(marital as string) as marital, \
                      cast(education as string) as education, \
                      cast(default as string) as default, \
                      cast(balance as int) as balance, \
                      cast(housing as string) as housing, \
                      cast(loan as string) as loan, \
                      cast(contact as string) as contact, \
                      cast(day as int) as day, \
                      cast(month as string) as month, \
                      cast(duration as int) as duration, \
                      cast(campaign as int) as campaign, \
                      cast(pdays as int) as pdays, \
                      cast(previous as int) as previous, \
                      cast(poutcome as string) as poutcome, \
                      cast(deposit as string) as deposit \
                  from df")
  
  # Data Types
  df2.dtypes
  
  [('id', 'int'),
   ('age', 'int'),
   ('job', 'string'),
   ('marital', 'string'),
   ('education', 'string'),
   ('default', 'string'),
   ('balance', 'int'),
   ('housing', 'string'),
   ('loan', 'string'),
   ('contact', 'string'),
   ('day', 'int'),
   ('month', 'string'),
   ('duration', 'int'),
   ('campaign', 'int'),
   ('pdays', 'int'),
   ('previous', 'int'),
   ('poutcome', 'string'),
   ('deposit', 'string')]
  
  
   # Build Pipeline (Error is Here)
  categorical_cols = ["job","marital","education","default","housing","loan","contact","month","poutcome"]
  numeric_cols = ["age", "balance", "day", "duration", "campaign", "pdays","previous"]
  
  stages = []
  
  stringIndexer = StringIndexer(inputCol=[cols for cols in categorical_cols],
                                outputCol=[cols + "_index" for cols in categorical_cols])
  
  encoder = OneHotEncoderEstimator(inputCols=[cols + "_index" for cols in categorical_cols],
                                   outputCols=[cols + "_classVec" for cols in categorical_cols])
  
  stages += [stringIndexer, encoder]
  
  label_string_id = StringIndexer(inputCol="deposit", outputCol="label")
  stages += [label_string_id]
  
  assembler_inputs = [cols + "_classVec" for cols in categorical_cols] + numeric_cols
  assembler = VectorAssembler(inputCols=assembler_inputs, outputCol="features")
  stages += [assembler]
  
  # Run Data Through Pipeline
  pipeline = Pipeline().setStages(stages)
  pipeline_model = pipeline.fit(df2)
  prepped_df = pipeline_model.transform(df2)
  </code></pre>
  
  <h2>Error</h2>
  
  <p>"TypeError: Invalid param value given for param "inputCols". Could not convert job_index to list of strings"</p>