dlpy.splitting.three_way_split

dlpy.splitting.three_way_split(tbl, valid_rate=20, test_rate=20, stratify=True, im_table=True, stratify_by='_label_', image_col='_image_', train_name=None, valid_name=None, test_name=None, **kwargs)

Split image data into training and testing sets

Parameters:

tbl : CASTable

The CAS table to split

valid_rate : double, optional

Specifies the proportion of the validation data set, e.g. 20 mean 20% of the images will be in the validation set.

test_rate : double, optional

Specifies the proportion of the testing data set, e.g. 20 mean 20% of the images will be in the testing set.
Note: the total of valid_rate and test_rate cannot be exceed 100

stratify : boolean, optional

If True stratify the sampling by the stratify_by column name If False do random sampling without stratification

im_table : boolean, optional

If True outputs are converted to an imageTable If False CASTables are returned with all columns

stratify_by : string, optional

The variable to stratify by

image_col : string

Name of image column if returning ImageTable

train_name : string

Specifies the output table name for the training set

valid_name : string

Specifies the output table name for the validation set

test_name : string

Specifies the output table name for the test set

kwargs : keyword arguments, optional

Additional keyword arguments to the sample.stratified or sample.src actions. For details see sample.stratifed and sample.srs

Returns

——-

( train CASTable, valid CASTable, test CASTable )