Utilities for Developers#

Reserved.

scikit-plots contains a number of utilities to help with development. These are located in scikitplot.utils, and include tools in a number of categories. All the following functions and classes are in the module scikitplot.utils.

Warning

These utilities are meant to be used internally within the scikit-plots package. They are not guaranteed to be stable between versions of scikit-plots. Backports, in particular, will be removed as the scikit-plots dependencies evolve.

Validation Tools#

These are tools used to check and validate input. When you write a function which accepts arrays, matrices, or sparse matrices as arguments, the following should be used when applicable.

assert_all_finite: Throw an error if array contains NaNs or Infs.
as_float_array: convert input to an array of floats. If a sparse matrix is passed, a sparse matrix will be returned.
check_array: check that input is a 2D array, raise error on sparse matrices. Allowed sparse matrix formats can be given optionally, as well as allowing 1D or N-dimensional arrays. Calls assert_all_finite by default.
check_X_y: check that X and y have consistent length, calls check_array on X, and column_or_1d on y. For multilabel classification or multitarget regression, specify multi_output=True, in which case check_array will be called on y.
indexable: check that all input arrays have consistent length and can be sliced or indexed using safe_index. This is used to validate input for cross-validation.
validation.check_memory checks that input is joblib.Memory-like, which means that it can be converted into a sklearn.utils.Memory instance (typically a str denoting the cachedir) or has the same interface.

If your code relies on a random number generator, it should never use functions like numpy.random.random or numpy.random.normal. This approach can lead to repeatability issues in unit tests. Instead, a numpy.random.RandomState object should be used, which is built from a random_state argument passed to the class or function. The function check_random_state, below, can then be used to create a random number generator object.

check_random_state: create a np.random.RandomState object from a parameter random_state.
- If random_state is None or np.random, then a randomly-initialized RandomState object is returned.
- If random_state is an integer, then it is used to seed a new RandomState object.
- If random_state is a RandomState object, then it is passed through.

For example:

>>> from scikitplot.utils import check_random_state
>>> random_state = 0
>>> random_state = check_random_state(random_state)
>>> random_state.rand(4)
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])

When developing your own scikit-plots compatible estimator, the following helpers are available.

validation.check_is_fitted: check that the estimator has been fitted before calling transform, predict, or similar methods. This helper allows to raise a standardized error message across estimator.
validation.has_fit_parameter: check that a given parameter is supported in the fit method of a given estimator.