[
https://issues.apache.org/jira/browse/SPARK-19962?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
yu peng updated SPARK-19962:
----------------------------
Description:
it's really useful to have something like
sklearn.feature_extraction.DictVectorizor
Since out features lives in json/data frame like format and
classifier/regressors only take vector input. so there is a gap between them.
something like
```
df = sqlCtx.createDataFrame([Row(age=1, gender='male', country='cn',
hobbies=['sing', 'dance']),Row(age=3, gender='female', country='us',
hobbies=['sing']), ])
import DictVectorizor
vec = DictVectorizor()
matrix = vec.fit_transform(df)
matrix.show()
|features|
|[1, 0, 1, 0, 1, 1, 1]|
|[3, 1, 0, 1, 0, 1, 1]|
vec.show()
|feature_name| feature_dimension|
|age|0|
|gender=female|1|
|gender=male|2|
|country=us|3|
|country=cn|4|
|hobbies=sing|5|
|hobbies=dance|6|
```
was:
it's really useful to have something like
sklearn.feature_extraction.DictVectorizor
Since out features lives in json/data frame like format and
classifier/regressors only take vector input. so there is a gap between them.
something like
```
df = sqlCtx.createDataFrame([Row(age=1, gender='male', country='cn'),Row(age=3,
gender='female', country='us'), ])
import DictVectorizor
vec = DictVectorizor()
matrix = vec.fit_transform(df)
matrix.show()
|features|
|[1, 0, 1, 0, 1]|
|[3, 1, 0, 1, 0]|
vec.show()
|feature_name| feature_dimension|
|age|0|
|gender=female|1|
|gender=male|2|
|country=us|3|
|country=cn|4|
```
> add DictVectorizor for DataFrame
> --------------------------------
>
> Key: SPARK-19962
> URL: https://issues.apache.org/jira/browse/SPARK-19962
> Project: Spark
> Issue Type: Wish
> Components: ML
> Affects Versions: 2.1.0
> Reporter: yu peng
> Labels: features
>
> it's really useful to have something like
> sklearn.feature_extraction.DictVectorizor
> Since out features lives in json/data frame like format and
> classifier/regressors only take vector input. so there is a gap between them.
> something like
> ```
> df = sqlCtx.createDataFrame([Row(age=1, gender='male', country='cn',
> hobbies=['sing', 'dance']),Row(age=3, gender='female', country='us',
> hobbies=['sing']), ])
> import DictVectorizor
> vec = DictVectorizor()
> matrix = vec.fit_transform(df)
> matrix.show()
> |features|
> |[1, 0, 1, 0, 1, 1, 1]|
> |[3, 1, 0, 1, 0, 1, 1]|
> vec.show()
> |feature_name| feature_dimension|
> |age|0|
> |gender=female|1|
> |gender=male|2|
> |country=us|3|
> |country=cn|4|
> |hobbies=sing|5|
> |hobbies=dance|6|
> ```
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