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StringIndexer-and-VectorIndexer.Rmd
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StringIndexer-and-VectorIndexer.Rmd
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---
title: "StringIndexer and VectorIndexer"
author: "Wenqiang Feng & Ming Chen"
date: "2/19/2017"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval = FALSE)
```
## **StringIndexer** and **VectorIndexer**
* **StringIndexer**
+ converts a single column to an *index column* (similar to a factor column in R)
* **VectorIndexer**
+ is used to index categorical predictors in a **featuresCol** column. Remember that **featuresCol** is a single column consisting of vectors (refer to [featuresCol and labelCol](featuresCol-and-labelCol.html)). Each row is a vector which contains values from each predictors.
+ if you have string type predictors, you will first need to use index those columns with **StringIndexer**. **featuresCol** contains vectors, and vectors does not contain string values.
## Example
Data resource: [http://www.ats.ucla.edu/stat/r/modules/dummy_vars.htm](http://www.ats.ucla.edu/stat/r/modules/dummy_vars.htm). I modified that *female* column and generate a new dataset called *hsb2_modified.csv*.
## Set up sparkContext and sparkSession
```{python}
# connecting to spark
from pyspark import SparkConf, SparkContext
## set up spark context
conf = SparkConf().setAppName("myApp")
sc = SparkContext(conf=conf)
# create sparksession object
from pyspark.sql import SparkSession
sparksession = SparkSession(sc)
```
## Import data
```{python}
hsb2 = sparksession.read.csv("data/hsb2_modified.csv", inferSchema=True, header=True)
hsb2.show(n=5)
```
```{python}
+---+------+----+---+------+----+----+-----+----+-------+-----+
| id|gender|race|ses|schtyp|prog|read|write|math|science|socst|
+---+------+----+---+------+----+----+-----+----+-------+-----+
| 70| male| 4| 1| 1| 1| 57| 52| 41| 47| 57|
|121|female| 4| 2| 1| 3| 68| 59| 53| 63| 61|
| 86| male| 4| 3| 1| 1| 44| 33| 54| 58| 31|
|141| male| 4| 3| 1| 3| 63| 44| 47| 53| 56|
|172| male| 4| 2| 1| 2| 47| 52| 57| 53| 61|
+---+------+----+---+------+----+----+-----+----+-------+-----+
```
## Use `StringIndexer` to index string column
```{python}
from pyspark.ml.feature import StringIndexer
indexer = StringIndexer(inputCol='gender', outputCol='indexed_gender')
model = indexer.fit(hsb2)
hsb2 = model.transform(hsb2)
hsb2.show(n=5)
```
```{python}
## see indexed_gender column
+---+------+----+---+------+----+----+-----+----+-------+-----+--------------+
| id|gender|race|ses|schtyp|prog|read|write|math|science|socst|indexed_gender|
+---+------+----+---+------+----+----+-----+----+-------+-----+--------------+
| 70| male| 4| 1| 1| 1| 57| 52| 41| 47| 57| 1.0|
|121|female| 4| 2| 1| 3| 68| 59| 53| 63| 61| 0.0|
| 86| male| 4| 3| 1| 1| 44| 33| 54| 58| 31| 1.0|
|141| male| 4| 3| 1| 3| 63| 44| 47| 53| 56| 1.0|
|172| male| 4| 2| 1| 2| 47| 52| 57| 53| 61| 1.0|
+---+------+----+---+------+----+----+-----+----+-------+-----+--------------+
```
## Transform data into `featuresCol` and `labelCol` structure
```{python}
from pyspark.ml.linalg import Vectors
from pyspark.sql import Row
new_hsb2 = hsb2.rdd.map(lambda row: Row(label=row['socst'],
features=Vectors.dense([
row['indexed_gender'],
row['race'],
row['ses'],
row['schtyp'],
row['prog'],
row['read'],
row['write'],
row['math'],
row['science']])
)).toDF()
new_hsb2.show(n=5)
```
```{python}
+--------------------+-----+
| features|label|
+--------------------+-----+
|[1.0,4.0,1.0,1.0,...| 57|
|[0.0,4.0,2.0,1.0,...| 61|
|[1.0,4.0,3.0,1.0,...| 31|
|[1.0,4.0,3.0,1.0,...| 56|
|[1.0,4.0,2.0,1.0,...| 61|
+--------------------+-----+
```
## Use `VectorIndexer` to index categorical predictors in `featuresCol`
```{python}
from pyspark.ml.feature import VectorIndexer
indexer = VectorIndexer(maxCategories=4, inputCol='features', outputCol='indexed_features')
model = indexer.fit(new_hsb2)
indexed_new_hsb2 = model.transform(new_hsb2)
indexed_new_hsb2.take(2)
```
```{python}
# before indexing featuresCol # after indexing featuresCol
+--------------------+-----+ +--------------------+-----+--------------------+
| features|label| | features|label| indexed_features|
+--------------------+-----+ +--------------------+-----+--------------------+
|[1.0,4.0,1.0,1.0,...| 57| |[1.0,4.0,1.0,1.0,...| 57|[1.0,3.0,0.0,0.0,...|
|[0.0,4.0,2.0,1.0,...| 61| |[0.0,4.0,2.0,1.0,...| 61|[0.0,3.0,1.0,0.0,...|
|[1.0,4.0,3.0,1.0,...| 31| |[1.0,4.0,3.0,1.0,...| 31|[1.0,3.0,2.0,0.0,...|
|[1.0,4.0,3.0,1.0,...| 56| |[1.0,4.0,3.0,1.0,...| 56|[1.0,3.0,2.0,0.0,...|
|[1.0,4.0,2.0,1.0,...| 61| |[1.0,4.0,2.0,1.0,...| 61|[1.0,3.0,1.0,0.0,...|
+--------------------+-----+ +--------------------+-----+--------------------+
```
* **Fitting a linear model**
```{python}
from pyspark.ml.regression import LinearRegression
lr = LinearRegression(featuresCol='indexed_features', labelCol='label')
model = lr.fit(indexed_new_hsb2)
model.transform(indexed_new_hsb2).show()
```
```{python}
+--------------------+-----+--------------------+------------------+
| features|label| indexed_features| prediction|
+--------------------+-----+--------------------+------------------+
|[1.0,4.0,1.0,1.0,...| 57|[1.0,3.0,0.0,0.0,...| 51.80048015803359|
|[0.0,4.0,2.0,1.0,...| 61|[0.0,3.0,1.0,0.0,...|58.243471181988404|
|[1.0,4.0,3.0,1.0,...| 31|[1.0,3.0,2.0,0.0,...| 45.60938102700539|
|[1.0,4.0,3.0,1.0,...| 56|[1.0,3.0,2.0,0.0,...| 53.16002885219301|
|[1.0,4.0,2.0,1.0,...| 61|[1.0,3.0,1.0,0.0,...| 50.59419095464209|
|[1.0,4.0,2.0,1.0,...| 61|[1.0,3.0,1.0,0.0,...|48.200748866519014|
|[1.0,3.0,2.0,1.0,...| 61|[1.0,2.0,1.0,0.0,...| 53.93023551618455|
|[1.0,1.0,2.0,1.0,...| 36|[1.0,0.0,1.0,0.0,...|43.258280997139416|
|[1.0,4.0,2.0,1.0,...| 51|[1.0,3.0,1.0,0.0,...| 58.89692949834014|
|[1.0,3.0,2.0,1.0,...| 51|[1.0,2.0,1.0,0.0,...| 54.8926363226898|
|[1.0,4.0,2.0,1.0,...| 61|[1.0,3.0,1.0,0.0,...| 50.950400342767|
|[1.0,4.0,2.0,1.0,...| 61|[1.0,3.0,1.0,0.0,...| 57.6279630554164|
|[1.0,4.0,3.0,1.0,...| 71|[1.0,3.0,2.0,0.0,...| 66.25089147947871|
|[1.0,4.0,3.0,1.0,...| 46|[1.0,3.0,2.0,0.0,...|59.341701186507144|
|[1.0,3.0,1.0,1.0,...| 56|[1.0,2.0,0.0,0.0,...| 49.98714372879461|
|[1.0,4.0,1.0,1.0,...| 56|[1.0,3.0,0.0,0.0,...|45.370862024502635|
|[1.0,4.0,3.0,1.0,...| 56|[1.0,3.0,2.0,0.0,...| 52.42836867774257|
|[1.0,4.0,2.0,2.0,...| 56|[1.0,3.0,1.0,1.0,...| 57.14432439272428|
|[1.0,4.0,3.0,1.0,...| 61|[1.0,3.0,2.0,0.0,...| 65.6544475938724|
|[1.0,4.0,2.0,1.0,...| 46|[1.0,3.0,1.0,0.0,...|50.080534041206725|
+--------------------+-----+--------------------+------------------+
```