TypeError: Column is not iterable while trying to typecast

from pyspark.ml.regression import LinearRegression
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.sql.types import DoubleType
for i in range(0, len(names)):
    df2=trainingDF
    numericCols = [c for c in trainingDF.columns if c not in {names[i]}]
    assembler = VectorAssembler(inputCols=numericCols, outputCol="features") 
    df = assembler.transform(df2)
    df=df.withColumnRenamed(names[i],"label")
    lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
    lrModel = lr.fit(df)
    evaluator = RegressionEvaluator()
    evaluator.setPredictionCol(names[i])
    evaluator.evaluate(df2[names[i]].cast(DoubleType()))
    rsq=evaluator.evaluate(df, {evaluator.metricName: "r2"})
    vif = round(1 / (1 - rsq), 2)
    print(vif)

I am getting an error on the following line. I want to pass all the columns of df2 as double values and typecast them to double.

evaluator.evaluate(df2[names[i]].cast(DoubleType()))

According to the documentation, the method evaluate takes a pyspark.sql.DataFrame object as the first parameter, but you have provided a column (df2[names[i]].cast(DoubleType()) is of Column data type, not DataFrame).

evaluate(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → float

Evaluates the output with optional parameters.

New in version 1.4.0.

Parameters:

• dataset : pyspark.sql.DataFrame
  a dataset that contains labels/observations and predictions

• params : dict, optional
  an optional param map that overrides embedded params

Returns:

• float
  metric