Spark 2.2.0: New Imputer to replace missing values

With the release of Spark 2.2.0, we can now use the newly implemented Imputer to replace missing values in our dataset. However, it only supports mean and median as the imputation strategies currently but not the most frequent. The default strategy is mean. (Note: scikit-learn provides all three different strategies).

See the Imputer class and the associated Jira ticket below

Example usage using Zillow Price Kaggle dataset:

val imputer = new Imputer()
imputer.setInputCols(Array("bedroomcnt", "bathroomcnt", "roomcnt", "calculatedfinishedsquarefeet", "taxamount", "taxvaluedollarcnt", "landtaxvaluedollarcnt", "structuretaxvaluedollarcnt")) 

imputer.setOutputCols(Array("bedroomcnt_out", "bathroomcnt_out", "roomcnt_out", "calculatedfinishedsquarefeet_out", "taxamount_out", "taxvaluedollarcnt_out", "landtaxvaluedollarcnt_out", "structuretaxvaluedollarcnt_out"))

However, I ran into this issue, it can’t handle column values of integer type. See this Jira ticket.

The good news is a pull request was created to fix the issue by converting integer type to double type during imputation. See the pull request below.

StringIndexer transform fails when column contains nulls

If you run into NullPointerException when using StringIndexer in Spark version < 2.2.0, this means that your input column contains null values. You would have to remove/impute these null values before using StringIndexer. See ticket below. Good news is this issue was fixed in Spark version 2.2.0

With the fix, we can specify how StringIndexer should handle null values, three different strategies are available as below.

handleInvalid=error: Throw an exception as before
handleInvalid=skip: Skip null values as well as unseen labels
handleInvalid=keep: Give null values an additional index as well as unseen labels

val codeIndexer = new StringIndexer().setInputCol("originalCode").setOutputCol("originalCodeCategory")




Zillow Price Kaggle Competition Part 2

From my last blog, I calculated the missing value percentage for every columns in the data. Next thing to do is to perform imputation of missing values in selected columns before model training.

However, I am going to skip doing in depth data cleaning and feature selection eg. removing outliers and calculating correlations between features, etc. I will do that in the next blog 😉

Instead I am going to use  the following features to build a model. These columns have low missing value percentage.


I replaced missing values in bedroomcnt with 3 and bathroomcnt with 2 (use most frequent value) and drop any row that has any missing values in the other columns.

In this experiment, I used Spark to train a Gradient Boost Tree Regression model. I built a Spark ML pipeline to perform hyperparameter tuning of GBT. Basically, it will test a grid of different hyperparameters and choose the best parameters based on the evaluation metric, RMSE.

val paramGrid = new ParamGridBuilder()
      .addGrid(gbt.maxDepth, Array(2,5))
      .addGrid(gbt.maxIter, Array(50,100))


Screen Shot 2017-08-07 at 12.37.39 AM

Screen Shot 2017-08-07 at 12.42.14 AM

Next I will need to go back to data cleaning and feature selection to choose better/more features to improve the model.

Zillow Prize Kaggle Competition: Exploratory Analysis Part 1

You can download the Zillow Price Kaggle datasets from
For this exploratory study of property dataset, I used Spark to calculate the missing value percentage for each column. As you can see below, as many as 20 columns have greater than 90% null/empty values. Examples: storytypeid, basementsqft, yardbuildingsqft.

(Column Name, Missing Value Percentage)


Screen Shot 2017-08-05 at 12.24.07 AM

Now I have identified the good columns (italics bold, less than 10% empty values) and bad columns. The next step is to determine if we can apply some sorts of missing values imputation to any of those problematic columns.

A few missing value imputation techniques are available, such as using mean, median, or most frequent value as replacement value.

Deprecated functions in org.apache.spark.sql. functions in Spark 2.0

I just moved some of my spark codes from 1.6.0 to 2.2.0 and discovered that some functions in org.apache.spark.sql.functions._ are being replaced/renamed.

To name a few, see below

1) rowNumber() is replaced by row_number()

import org.apache.spark.sql.functions._
* @group window_funcs
* @deprecated As of 1.6.0, replaced by `row_number`. This will be removed in Spark 2.0.
@deprecated("Use row_number. This will be removed in Spark 2.0.", "1.6.0")
def rowNumber(): Column = row_number()

2) isNaN is replaced by isnan

   * @group normal_funcs
   * @deprecated As of 1.6.0, replaced by `isnan`. This will be removed in Spark 2.0.
  @deprecated("Use isnan. This will be removed in Spark 2.0.", "1.6.0")
  def isNaN(e: Column): Column = isnan(e)

3) inputFileName() is replaced by input_file_name

   * @group normal_funcs
   * @deprecated As of 1.6.0, replaced by `input_file_name`. This will be removed in Spark 2.0.
  @deprecated("Use input_file_name. This will be removed in Spark 2.0.", "1.6.0")
  def inputFileName(): Column = input_file_name()

To get the full list of all the replaced/renamed functions, refer to this code