Parameter stratify from method train test split scikit Learn

Question

I am trying to use train test split from package scikit Learn  but I am having trouble with parameter stratify  Hereafter is the code  from sklearn import cross validation  datasets   X   iris data    2  y   iris target  cross validation train test split X y stratify y   However  I keep getting the following problem  raise TypeError  quot Invalid parameters passed   s quot    str options   TypeError  Invalid parameters passed    stratify   array  0  0  0  0  0  0  0  0  0  0  0   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1   1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  2   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2     Does someone have an idea what is going on  Below is the function documentation         stratify   array-like or None  default is None  If not None  data is split in a stratified fashion  using this as the labels array  New in version 0 17  stratify splitting

User · Answer

For my future self who comes here via Google:

train_test_split is now in model_selection, hence:

from sklearn.model_selection import train_test_split

# given:
# features: xs
# ground truth: ys

x_train, x_test, y_train, y_test = train_test_split(xs, ys,
                                                    test_size=0.33,
                                                    random_state=0,
                                                    stratify=ys)

is the way to use it. Setting the random_state is desirable for reproducibility.

User · Answer

This stratify parameter makes a split so that the proportion of values in the sample produced will be the same as the proportion of values provided to parameter stratify    For example  if variable y is a binary categorical variable with values 0 and 1 and there are 25  of zeros and 75  of ones  stratify y will make sure that your random split has 25  of 0 s and 75  of 1 s

User · Answer

Try running this code  it  just works    from sklearn import cross validation  datasets   iris   datasets load iris    X   iris data    2  y   iris target  x train  x test  y train  y test   cross validation train test split X y train size  8  stratify y   y test  array  0  0  0  0  2  2  1  0  1  2  2  0  0  1  0  1  1  2  1  2  0  2  2         1  2  1  1  0  2  1

User · Answer

In this context  stratification means that the train test split method returns training and test subsets that have the same proportions of class labels as the input dataset

User · Answer

Scikit-Learn is just telling you it doesn t recognise the argument  stratify   not that you re using it incorrectly  This is because the parameter was added in version 0 17 as indicated in the documentation you quoted   So you just need to update Scikit-Learn

[split] Parameter "stratify" from method "train_test_split" (scikit Learn)

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