""" Utilities fo data validation """
# Authors:
# Wilson Rocha Lacerda Junior <wilsonrljr@outlook.com>
# License: BSD 3 clause
import numpy as np
import numbers
# copy-pasted/adapted from scikit-learn utils/validation.py
[docs]def check_random_state(seed):
"""Turn `seed` into a `np.random.RandomState` instance.
Parameters
----------
seed : {None, int, `numpy.random.Generator`,
`numpy.random.RandomState`}, optional
If `seed` is None (or `np.random`), the `numpy.random.RandomState`
singleton is used.
If `seed` is an int, a new ``RandomState`` instance is used,
seeded with `seed`.
If `seed` is already a ``Generator`` or ``RandomState`` instance then
that instance is used.
Returns
-------
seed : {`numpy.random.Generator`, `numpy.random.RandomState`}
Random number generator.
"""
if seed is None or seed is np.random:
return np.random.mtrand._rand
if isinstance(seed, (numbers.Integral, np.integer)):
return np.random.default_rng(seed)
if isinstance(seed, (np.random.RandomState, np.random.Generator)):
return seed
raise ValueError(
"%r cannot be used to seed a numpy.random.RandomState" " instance" % seed
)
def _num_features(X):
return X.shape[1]
def _check_positive_int(value, name):
if not isinstance(value, int) or value < 1:
raise ValueError(f"{name} must be integer and > zero. Got {value}")
else:
pass
[docs]def check_infinity(X, y):
"""Check that X and y have no NaN or Inf samples.
If there is any NaN or Inf samples a ValueError is raised.
Parameters
----------
X : ndarray of floats
The input data.
y : ndarray of floats
The output data.
"""
if np.isinf(X).any():
msg_error = (
f"Input contains invalid values (e.g. NaN, Inf) on "
f"index {np.argwhere(np.isinf(X))}"
)
raise ValueError(msg_error)
if np.isinf(y).any():
msg_error = (
f"Output contains invalid values (e.g Inf) on "
f"index {np.argwhere(np.isinf(y))}"
)
raise ValueError(msg_error)
[docs]def check_nan(X, y):
"""Check that X and y have no NaN or Inf samples.
If there is any NaN or Inf samples a ValueError is raised.
Parameters
----------
X : ndarray of floats
The input data.
y : ndarray of floats
The output data.
"""
if np.isnan(X).any():
msg_error = (
f"Input contains invalid values (e.g. NaN, Inf) on "
f"index {np.argwhere(np.isnan(X))}"
)
raise ValueError(msg_error)
if not ~np.isnan(y).any():
msg_error = (
f"Output contains invalid values (e.g. NaN, Inf) on "
f"index {np.argwhere(np.isnan(y))}"
)
raise ValueError(msg_error)
[docs]def check_length(X, y):
"""Check that X and y have the same number of samples.
If the length of X and y are different a ValueError is raised.
Parameters
----------
X : ndarray of floats
The input data.
y : ndarray of floats
The output data.
"""
if X.shape[0] != y.shape[0]:
msg_error = (
f"Input and output data must have the same number of "
f"samples. X has dimension {X.shape} and "
f"y has dimension {y.shape}"
)
raise ValueError(msg_error)
[docs]def check_dimension(X, y):
"""Check if X and y have only real values.
If there is any string or object samples a ValueError is raised.
Parameters
----------
X : ndarray of floats
The input data.
y : ndarray of floats
The output data.
"""
if X.ndim == 0:
raise ValueError(
(
f"Input must be a 2d array, got scalar instead.\n"
f"Reshape your data using array.reshape(-1, 1)"
)
)
if X.ndim == 1:
raise ValueError(
(
f"Input must be a 2d array, got 1d array instead.\n"
f"Reshape your data using array.reshape(-1, 1)"
)
)
if y.ndim == 0:
raise ValueError(
(
f"Output must be a 2d array, got scalar instead.\n"
f"Reshape your data using array.reshape(-1, 1)"
)
)
if y.ndim == 1:
raise ValueError(
(
f"Output must be a 2d array, got 1d array instead.\n"
f"Reshape your data using array.reshape(-1, 1)"
)
)
[docs]def check_X_y(X, y):
"""Validate input and output data using some crucial tests.
Parameters
----------
X : ndarray of floats
The input data.
y : ndarray of floats
The output data.
"""
check_length(X, y)
check_dimension(X, y)
check_infinity(X, y)
check_nan(X, y)
