

In [1]:

    
# create entry points to spark
try:
    sc.stop()
except:
    pass
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
sc=SparkContext()
spark = SparkSession(sparkContext=sc)

Convert continuous variables to categorical variables

There are two functions we can use to split a continuous variable into categories:

pyspark.ml.feature.Binarizer: split a column of continuous features given a threshold
pyspark.ml.feature.Bucktizer: split a column of continuous features into categories given several breaking points.
- with n+1 split points, there are n categories (buckets).

Create some data



In [2]:

    
import numpy as np
import pandas as pd
np.random.seed(seed=1234)
pdf = pd.DataFrame({
        'x1': np.random.randn(10),
        'x2': np.random.rand(10)*10
    })
np.random.seed(seed=None)
df = spark.createDataFrame(pdf)
df.show()









    



+--------------------+------------------+
|                  x1|                x2|
+--------------------+------------------+
| 0.47143516373249306| 6.834629351721363|
| -1.1909756947064645| 7.127020269829002|
|  1.4327069684260973|3.7025075479039495|
| -0.3126518960917129| 5.611961860656249|
| -0.7205887333650116| 5.030831653078097|
|  0.8871629403077386|0.1376844959068224|
|  0.8595884137174165| 7.728266216123741|
| -0.6365235044173491| 8.826411906361166|
|0.015696372114428918| 3.648859839013723|
| -2.2426849541854055| 6.153961784334937|
+--------------------+------------------+

Binarize the column x1 and Bucketize the column x2



In [3]:

    
from pyspark.ml.feature import Binarizer, Bucketizer
# threshold = 0 for binarizer
binarizer = Binarizer(threshold=0, inputCol='x1', outputCol='x1_new')
# provide 5 split points to generate 4 buckets
bucketizer = Bucketizer(splits=[0, 2.5, 5, 7.5, 10], inputCol='x2', outputCol='x2_new')

# pipeline stages
from pyspark.ml import Pipeline
stages = [binarizer, bucketizer]
pipeline = Pipeline(stages=stages)

# fit the pipeline model and transform the data
pipeline.fit(df).transform(df).show()









    



+--------------------+------------------+------+------+
|                  x1|                x2|x1_new|x2_new|
+--------------------+------------------+------+------+
| 0.47143516373249306| 6.834629351721363|   1.0|   2.0|
| -1.1909756947064645| 7.127020269829002|   0.0|   2.0|
|  1.4327069684260973|3.7025075479039495|   1.0|   1.0|
| -0.3126518960917129| 5.611961860656249|   0.0|   2.0|
| -0.7205887333650116| 5.030831653078097|   0.0|   2.0|
|  0.8871629403077386|0.1376844959068224|   1.0|   0.0|
|  0.8595884137174165| 7.728266216123741|   1.0|   3.0|
| -0.6365235044173491| 8.826411906361166|   0.0|   3.0|
|0.015696372114428918| 3.648859839013723|   1.0|   1.0|
| -2.2426849541854055| 6.153961784334937|   0.0|   2.0|
+--------------------+------------------+------+------+



In [ ]: