How to retrieve the random_state of sklearn.model_selection.train_test_split?

Question

How to retrieve the random state of sklearn.model_selection.train_test_split?

Without setting the random_state, I split my dataset with train_test_split. Because the machine learning model trained on the split dataset performs quite well, I want to retrieve the random_state that was used to split the dataset. Is there something like numpy.random.get_state()

Answer 1

What do you mean?

If you wanna know which random_state you are using, you have to use random_state while running the function, for example:

X_train, X_test, y_train, y_test = train_test_split(
...     X, y, test_size=0.33, random_state=42)

by default its set to none see the docs.

Here are also further information to random_state.

Or do you mean this?

Answer 2

If you only have an old notebook showing a slice of one+ of the train/test subsets (eg X_test[0:5], y_train[-5:], etc), but you know the other parameters (eg [test_size | train_size, shuffle, stratify]) of the train_test_split() call and can perfectly recreate X and y, you could try brute-forcing it by generating new splits with different random_state seeds and comparing the split to your known subset-slice and recording any random_state values producing matching (or close-enough that differences could just be floating-point weirdness) subset-slice values.

target_y_train = np.array([-5.482, -11.165, -13.926,  -7.534, -8.323])
possible_random_state_values = []
for i in range(0, 1000):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=i)
    if all(np.isclose(y_train[0:5], target_y_train)):
        possible_random_state_values.append(i)
        print(f"Possible random state value found: {i}")

If you don't get any possible seeds from the (0, 1000] range, increase the higher range. And when you get values, you can plug them into train_test_split(), compare other subset_slices if you have any, rerun your model training pipeline, and compare your output metrics.