Role of OneHotEncoder and Pipelines in PySpark ML Feature - Part 2

Role of OneHotEncoder and Pipelines in PySpark ML Feature - Part 2

Part 1 - What is StringIndexer?

We have already discussed regarding StringIndexer (link)

What is OneHotEncoder?

class pyspark.ml.feature.OneHotEncoder(inputCols=None, outputCols=None, handleInvalid='error', dropLast=True, inputCol=None, outputCol=None) - One Hot Encoding is a technique for converting categorical attributes into a binary vector.

A one-hot encoder that maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index.

For example with 5 categories, an input value of 2.0 would map to an output vector of [0.0, 0.0, 1.0, 0.0].

The last category is not included by default (configurable via dropLast), because it makes the vector entries sum up to one, and hence linearly dependent. So an input value of 4.0 maps to [0.0, 0.0, 0.0, 0.0].

Let us see an example

Create SparkSession

In [2]:
#import SparkSession
from pyspark.sql import SparkSession

SparkSession is an entry point to Spark to work with RDD, DataFrame, and Dataset. To create SparkSession in Python, we need to use the builder() method and calling getOrCreate() method.

If SparkSession already exists it returns otherwise create a new SparkSession.

In [3]:
spark = SparkSession.builder.appName('xvspark').getOrCreate()

Create dataframe by declaring the schema

In [4]:
from pyspark.sql.types import *

StructType class to define the structure of the DataFrame.

In [5]:
#create the structure of schema
schema = StructType().add("id","integer").add("name","string").add("qualification","string").add("age", "integer").add("gender", "string")
In [6]:
#create data
data = [
    (1,'John',"B.A.", 20, "Male"),
    (2,'Martha',"B.Com.", 20, "Female"),
    (3,'Mona',"B.Com.", 21, "Female"),
    (4,'Harish',"B.Sc.", 22, "Male"),
    (5,'Jonny',"B.A.", 22, "Male"),
    (6,'Maria',"B.A.", 23, "Female"),
    (7,'Monalisa',"B.A.", 21, "Female")
]
In [7]:
#create dataframe
df = spark.createDataFrame(data, schema=schema)
In [8]:
#columns of dataframe
df.columns
Out[8]:
['id', 'name', 'qualification', 'age', 'gender']
In [9]:
df.show()

+---+--------+-------------+---+------+
| id|    name|qualification|age|gender|
+---+--------+-------------+---+------+
|  1|    John|         B.A.| 20|  Male|
|  2|  Martha|       B.Com.| 20|Female|
|  3|    Mona|       B.Com.| 21|Female|
|  4|  Harish|        B.Sc.| 22|  Male|
|  5|   Jonny|         B.A.| 22|  Male|
|  6|   Maria|         B.A.| 23|Female|
|  7|Monalisa|         B.A.| 21|Female|
+---+--------+-------------+---+------+

Apply OneHotEncoder to qualification and gender column

We can not apply OneHotEncoder to string columns directly. We need to first convert string columns to numeric value. For that we will use StringIndexer. After that we can apply OneHotEncoder.

Apply StringIndexer to qualification column

In [10]:
#import required libraries
from pyspark.ml.feature import StringIndexer
In [11]:
qualification_indexer = StringIndexer(inputCol="qualification", outputCol="qualificationIndex")

#Fits a model to the input dataset with optional parameters.
df1 = qualification_indexer.fit(df).transform(df)
df1.show()

+---+--------+-------------+---+------+------------------+
| id|    name|qualification|age|gender|qualificationIndex|
+---+--------+-------------+---+------+------------------+
|  1|    John|         B.A.| 20|  Male|               0.0|
|  2|  Martha|       B.Com.| 20|Female|               1.0|
|  3|    Mona|       B.Com.| 21|Female|               1.0|
|  4|  Harish|        B.Sc.| 22|  Male|               2.0|
|  5|   Jonny|         B.A.| 22|  Male|               0.0|
|  6|   Maria|         B.A.| 23|Female|               0.0|
|  7|Monalisa|         B.A.| 21|Female|               0.0|
+---+--------+-------------+---+------+------------------+

"B.A." gets index 0 because it is the most frequent, then "B.Com" gets index 1 and "B.Sc." gets index 2.

Apply StringIndexer to gender column

In [12]:
gender_indexer = StringIndexer(inputCol="gender", outputCol="genderIndex")

#Fits a model to the input dataset with optional parameters.
df2 = gender_indexer.fit(df).transform(df)
df2.show()

+---+--------+-------------+---+------+-----------+
| id|    name|qualification|age|gender|genderIndex|
+---+--------+-------------+---+------+-----------+
|  1|    John|         B.A.| 20|  Male|        1.0|
|  2|  Martha|       B.Com.| 20|Female|        0.0|
|  3|    Mona|       B.Com.| 21|Female|        0.0|
|  4|  Harish|        B.Sc.| 22|  Male|        1.0|
|  5|   Jonny|         B.A.| 22|  Male|        1.0|
|  6|   Maria|         B.A.| 23|Female|        0.0|
|  7|Monalisa|         B.A.| 21|Female|        0.0|
+---+--------+-------------+---+------+-----------+

Apply OneHotEncoder to qualificationIndex column

In [13]:
from pyspark.ml.feature import OneHotEncoder
In [14]:
#onehotencoder to qualificationIndex
onehotencoder_qualification_vector = OneHotEncoder(inputCol="qualificationIndex", outputCol="qualification_vec")
df11 = onehotencoder_qualification_vector.fit(df1).transform(df1)
In [15]:
df11.show()

+---+--------+-------------+---+------+------------------+-----------------+
| id|    name|qualification|age|gender|qualificationIndex|qualification_vec|
+---+--------+-------------+---+------+------------------+-----------------+
|  1|    John|         B.A.| 20|  Male|               0.0|    (2,[0],[1.0])|
|  2|  Martha|       B.Com.| 20|Female|               1.0|    (2,[1],[1.0])|
|  3|    Mona|       B.Com.| 21|Female|               1.0|    (2,[1],[1.0])|
|  4|  Harish|        B.Sc.| 22|  Male|               2.0|        (2,[],[])|
|  5|   Jonny|         B.A.| 22|  Male|               0.0|    (2,[0],[1.0])|
|  6|   Maria|         B.A.| 23|Female|               0.0|    (2,[0],[1.0])|
|  7|Monalisa|         B.A.| 21|Female|               0.0|    (2,[0],[1.0])|
+---+--------+-------------+---+------+------------------+-----------------+
In [16]:
#onehotencoder to genderIndex
onehotencoder_gender_vector = OneHotEncoder(inputCol="genderIndex", outputCol="gender_vec")
df12 = onehotencoder_gender_vector.fit(df2).transform(df2)
In [17]:
df12.show()

+---+--------+-------------+---+------+-----------+-------------+
| id|    name|qualification|age|gender|genderIndex|   gender_vec|
+---+--------+-------------+---+------+-----------+-------------+
|  1|    John|         B.A.| 20|  Male|        1.0|    (1,[],[])|
|  2|  Martha|       B.Com.| 20|Female|        0.0|(1,[0],[1.0])|
|  3|    Mona|       B.Com.| 21|Female|        0.0|(1,[0],[1.0])|
|  4|  Harish|        B.Sc.| 22|  Male|        1.0|    (1,[],[])|
|  5|   Jonny|         B.A.| 22|  Male|        1.0|    (1,[],[])|
|  6|   Maria|         B.A.| 23|Female|        0.0|(1,[0],[1.0])|
|  7|Monalisa|         B.A.| 21|Female|        0.0|(1,[0],[1.0])|
+---+--------+-------------+---+------+-----------+-------------+

One hot encoding of a numeric column

Direct one-hot-encoding without indexing

In [18]:
onehotencoder_age_vector = OneHotEncoder(inputCol="age", outputCol="age_vec")
df13 = onehotencoder_age_vector.fit(df2).transform(df2)
In [19]:
df13.show()

+---+--------+-------------+---+------+-----------+---------------+
| id|    name|qualification|age|gender|genderIndex|        age_vec|
+---+--------+-------------+---+------+-----------+---------------+
|  1|    John|         B.A.| 20|  Male|        1.0|(23,[20],[1.0])|
|  2|  Martha|       B.Com.| 20|Female|        0.0|(23,[20],[1.0])|
|  3|    Mona|       B.Com.| 21|Female|        0.0|(23,[21],[1.0])|
|  4|  Harish|        B.Sc.| 22|  Male|        1.0|(23,[22],[1.0])|
|  5|   Jonny|         B.A.| 22|  Male|        1.0|(23,[22],[1.0])|
|  6|   Maria|         B.A.| 23|Female|        0.0|     (23,[],[])|
|  7|Monalisa|         B.A.| 21|Female|        0.0|(23,[21],[1.0])|
+---+--------+-------------+---+------+-----------+---------------+

Direct one-hot-encoding after indexing

In [22]:
age_indexer = StringIndexer(inputCol="age", outputCol="ageIndex")
df13 = age_indexer.fit(df).transform(df)


onehotencoder_age_vector = OneHotEncoder(inputCol="ageIndex", outputCol="age_vec")
df14 = onehotencoder_age_vector.fit(df13).transform(df13)

df14.show()

+---+--------+-------------+---+------+--------+-------------+
| id|    name|qualification|age|gender|ageIndex|      age_vec|
+---+--------+-------------+---+------+--------+-------------+
|  1|    John|         B.A.| 20|  Male|     0.0|(3,[0],[1.0])|
|  2|  Martha|       B.Com.| 20|Female|     0.0|(3,[0],[1.0])|
|  3|    Mona|       B.Com.| 21|Female|     1.0|(3,[1],[1.0])|
|  4|  Harish|        B.Sc.| 22|  Male|     2.0|(3,[2],[1.0])|
|  5|   Jonny|         B.A.| 22|  Male|     2.0|(3,[2],[1.0])|
|  6|   Maria|         B.A.| 23|Female|     3.0|    (3,[],[])|
|  7|Monalisa|         B.A.| 21|Female|     1.0|(3,[1],[1.0])|
+---+--------+-------------+---+------+--------+-------------+

Using Pipeline

In [23]:
#import module
from pyspark.ml import Pipeline

Reload Data

In [24]:
schema = StructType().add("id","integer").add("name","string").add("qualification","string").add("age", "integer").add("gender", "string")
df = spark.createDataFrame(data, schema=schema)

Create Pipeline and pass all stages

In [25]:
qualification_indexer = StringIndexer(inputCol="qualification", outputCol="qualificationIndex")
gender_indexer = StringIndexer(inputCol="gender", outputCol="genderIndex")
age_indexer = StringIndexer(inputCol="age", outputCol="ageIndex")

onehotencoder_qualification_vector = OneHotEncoder(inputCol="qualificationIndex", outputCol="qualification_vec")
onehotencoder_gender_vector = OneHotEncoder(inputCol="genderIndex", outputCol="gender_vec")
onehotencoder_age_vector = OneHotEncoder(inputCol="ageIndex", outputCol="age_vec")


pipeline = Pipeline(stages=[qualification_indexer, 
                            gender_indexer, 
                            age_indexer,
                            onehotencoder_qualification_vector, 
                            onehotencoder_gender_vector,
                            onehotencoder_age_vector
                           ])
In [27]:
df_transformed = pipeline.fit(df).transform(df)
df_transformed.show()

+---+--------+-------------+---+------+------------------+-----------+--------+-----------------+-------------+-------------+
| id|    name|qualification|age|gender|qualificationIndex|genderIndex|ageIndex|qualification_vec|   gender_vec|      age_vec|
+---+--------+-------------+---+------+------------------+-----------+--------+-----------------+-------------+-------------+
|  1|    John|         B.A.| 20|  Male|               0.0|        1.0|     0.0|    (2,[0],[1.0])|    (1,[],[])|(3,[0],[1.0])|
|  2|  Martha|       B.Com.| 20|Female|               1.0|        0.0|     0.0|    (2,[1],[1.0])|(1,[0],[1.0])|(3,[0],[1.0])|
|  3|    Mona|       B.Com.| 21|Female|               1.0|        0.0|     1.0|    (2,[1],[1.0])|(1,[0],[1.0])|(3,[1],[1.0])|
|  4|  Harish|        B.Sc.| 22|  Male|               2.0|        1.0|     2.0|        (2,[],[])|    (1,[],[])|(3,[2],[1.0])|
|  5|   Jonny|         B.A.| 22|  Male|               0.0|        1.0|     2.0|    (2,[0],[1.0])|    (1,[],[])|(3,[2],[1.0])|
|  6|   Maria|         B.A.| 23|Female|               0.0|        0.0|     3.0|    (2,[0],[1.0])|(1,[0],[1.0])|    (3,[],[])|
|  7|Monalisa|         B.A.| 21|Female|               0.0|        0.0|     1.0|    (2,[0],[1.0])|(1,[0],[1.0])|(3,[1],[1.0])|
+---+--------+-------------+---+------+------------------+-----------+--------+-----------------+-------------+-------------+

You can convert it to Pandas DataFrame

In [29]:
df_transformed.toPandas()
Out[29]:
id name qualification age gender qualificationIndex genderIndex ageIndex qualification_vec gender_vec age_vec
0 1 John B.A. 20 Male 0.0 1.0 0.0 (1.0, 0.0) (0.0) (1.0, 0.0, 0.0)
1 2 Martha B.Com. 20 Female 1.0 0.0 0.0 (0.0, 1.0) (1.0) (1.0, 0.0, 0.0)
2 3 Mona B.Com. 21 Female 1.0 0.0 1.0 (0.0, 1.0) (1.0) (0.0, 1.0, 0.0)
3 4 Harish B.Sc. 22 Male 2.0 1.0 2.0 (0.0, 0.0) (0.0) (0.0, 0.0, 1.0)
4 5 Jonny B.A. 22 Male 0.0 1.0 2.0 (1.0, 0.0) (0.0) (0.0, 0.0, 1.0)
5 6 Maria B.A. 23 Female 0.0 0.0 3.0 (1.0, 0.0) (1.0) (0.0, 0.0, 0.0)
6 7 Monalisa B.A. 21 Female 0.0 0.0 1.0 (1.0, 0.0) (1.0) (0.0, 1.0, 0.0)
In [ ]:

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