Merge pull request #5 from HowardRiddiough/update-readme

Updated README

Author: HowardRiddiough

README.md

@@ -11,18 +11,18 @@ Making predictions in PySpark using sophistaicated python ml is unlocked using o
 
 `spark_predict` is a wrapper around a `pandas_udf`, a wrapper is used to enable a python ml model to be passed to the `pandas_udf`.
 
- def spark_predict(model, *cols) -> pyspark.sql.column:
-  """This function deploys python ml in PySpark using the `predict` method of the `model` parameter.
+ def spark_predict(model, cols) -> pyspark.sql.column:
+  """This function deploys python ml in PySpark using the `predict` method of `model.
 
   Args:
   model: python ml model with sklearn API
-  *cols (list-like): Features used for predictions, required to be present as columns in the spark DataFrame used to make predictions.
+  cols (list-like): Features used for predictions, required to be present as columns in the spark DataFrame used to make predictions.
   """
   @sf.pandas_udf(returnType=DoubleType())
   def predict_pandas_udf(*cols):
   # cols will be a tuple of pandas.Series here.
-  X = pd.concat(cols, axis=1)
-  return pd.Series(model.predict(X))
+  x = pd.concat(cols, axis=1)
+  return pd.Series(model.predict(x))
 
   return predict_pandas_udf(*cols)
 
@@ -40,4 +40,4 @@ The code used in the [deploying-python-ml-in-pyspark](deploying-python-ml-in-pys
 
 # Further Reading
  - The code used in is based on the excellent excellent blog post ["Prediction at Scale with scikit-learn and PySpark Pandas UDFs"](https://medium.com/civis-analytics/prediction-at-scale-with-scikit-learn-and-pyspark-pandas-udfs-51d5ebfb2cd8) written by **Michael Heilman**.
-- [sklearn](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html) has more information on column transformers with mixed types.
+- [sklearn](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html) has more information on column transformers with mixed types.