from zlib import crc32
def is_id_in_test_set(identifier, test_ratio):
return crc32(np.int64(identifier)) < test_ratio * 2**32
def split_data_with_id_hash(data, test_ratio, id_column):
ids = data[id_column]
in_test_set = ids.apply(lambda id_: is_id_in_test_set(id_, test_ratio))
return data.loc[~in_test_set], data.loc[in_test_set]
housing_with_id = housing.reset_index() # adds an `index` column
train_set, test_set = split_data_with_id_hash(housing_with_id, 0.2, "index")
book says
compute a hash of each instance’s identifier and put that instance in the test set if the hash is lower than or equal to 20% of the maximum hash value. This ensures that the test set will remain consistent across multiple runs, even if you refresh the dataset. The new test set will contain 20% of the new instances, but it will not contain any instance that was previously in the training set.
I don't understand how if the identifier is less than 20% of the maximum hash value will guarantee that an instance does not repeat in the test set
