Source code for

# Copyright 2022 The SQLNet Company GmbH
# This file is licensed under the Elastic License 2.0 (ELv2).
# Refer to the LICENSE.txt file in the root of the repository
# for details.

Splits data at random.

import numbers

import numpy as np

from import StringColumnView
from import random as random_col
from import from_value
from import _is_typed_list

[docs]def random( seed=5849, train=0.8, test=0.2, validation=0, **kwargs: float ) -> StringColumnView: """ Returns a :class:`` that can be used to randomly divide data into training, testing, validation or other sets. Args: seed (int): Seed used for the random number generator. train (float, optional): The share of random samples assigned to the training set. validation (float, optional): The share of random samples assigned to the validation set. test (float, optional): The share of random samples assigned to the test set. kwargs (float, optional): Any other sets you would like to assign. You can name these sets whatever you want to (in our example, we called it 'other'). Example: .. code-block:: python split = train=0.8, test=0.1, validation=0.05, other=0.05 ) train_set = data_frame[split=='train'] validation_set = data_frame[split=='validation'] test_set = data_frame[split=='test'] other_set = data_frame[split=='other'] """ values = np.asarray([train, validation, test] + list(kwargs.values())) if not _is_typed_list(values.tolist(), numbers.Real): raise ValueError("All values must be real numbers.") if np.abs(np.sum(values) - 1.0) > 0.0001: raise ValueError( "'train', 'validation', 'test' and all other sets must add up to 1, " + "but add up to " + str(np.sum(values)) + "." ) upper_bounds = np.cumsum(values) lower_bounds = upper_bounds - values names = ["train", "validation", "test"] + list(kwargs.keys()) col: StringColumnView = from_value("train") # type: ignore assert isinstance(col, StringColumnView), "Should be a StringColumnView" for i in range(len(names)): col = col.update( # type: ignore (random_col(seed=seed) >= lower_bounds[i]) # type: ignore & (random_col(seed=seed) < upper_bounds[i]), names[i], ) return col