sklearn stratified sampling based on a column

Question

I have a fairly large CSV file containing amazon review data which I read into a pandas data frame. I want to split the data 80-20(train-test) but while doing so I want to ensure that the split data is proportionally representing the values of one column (Categories), i.e all the different category of reviews are present both in train and test data proportionally.

The data looks like this:

**ReviewerID**       **ReviewText**        **Categories**       **ProductId**  1212                   good product         Mobile               14444425 1233                   will buy again       drugs                324532 5432                   not recomended       dvd                  789654123  

Im using the following code to do so:

import pandas as pd Meta = pd.read_csv('C:\\Users\\xyz\\Desktop\\WM Project\\Joined.csv') import numpy as np from sklearn.cross_validation import train_test_split  train, test = train_test_split(Meta.categories, test_size = 0.2, stratify=y) 

it gives the following error

NameError: name 'y' is not defined 

As I'm relatively new to python I cant figure out what I'm doing wrong or whether this code will stratify based on column categories. It seems to work fine when i remove the stratify option as well as the categories column from train-test split.

Any help will be appreciated.

Answer 1

    >>> import pandas as pd     >>> Meta = pd.read_csv('C:\\Users\\*****\\Downloads\\so\\Book1.csv')     >>> import numpy as np     >>> from sklearn.model_selection import train_test_split     >>> y = Meta.pop('Categories')     >>> Meta         ReviewerID      ReviewText  ProductId         0        1212    good product   14444425         1        1233  will buy again     324532         2        5432  not recomended  789654123     >>> y         0    Mobile         1     drugs         2       dvd         Name: Categories, dtype: object     >>> X = Meta     >>> X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42, stratify=y)     >>> X_test         ReviewerID    ReviewText  ProductId         0        1212  good product   14444425 

Answer 2

sklearn.model_selection.train_test_split

stratify : array-like or None (default is None)

If not None, data is split in a stratified fashion, using this as the class labels.

Along the API docs, I think you have to try like X_train, X_test, y_train, y_test = train_test_split(Meta_X, Meta_Y, test_size = 0.2, stratify=Meta_Y).

Meta_X, Meta_Y should be assigned properly by you(I think Meta_Y should be Meta.categories based on your code).