9. Utilities for Developers¶
Imbalanced-learn contains a number of utilities to help with development. These are
imblearn.utils, and include tools in a number of categories.
All the following functions and classes are in the module
These utilities are meant to be used internally within the imbalanced-learn package. They are not guaranteed to be stable between versions of imbalanced-learn. Backports, in particular, will be removed as the imbalanced-learn dependencies evolve.
9.1. 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.
check_neighbors_object: Check the objects is consistent to be a NN.
check_target_type: Check the target types to be conform to the current sam plers.
check_sampling_strategy: Checks that sampling target is onsistent with the type and return a dictionary containing each targeted class with its corresponding number of pixel.
deprecate_parameter the rest of this section is taken from
scikit-learn. Please refer to their original documentation.
If any publicly accessible method, function, attribute or parameter
is renamed, we still support the old one for two releases and issue
a deprecation warning when it is called/passed/accessed.
E.g., if the function
zero_one is renamed to
we add the decorator
zero_one and call
zero_one_loss from that function:
from ..utils import deprecated def zero_one_loss(y_true, y_pred, normalize=True): # actual implementation pass @deprecated("Function 'zero_one' was renamed to 'zero_one_loss' " "in version 0.13 and will be removed in release 0.15. " "Default behavior is changed from 'normalize=False' to " "'normalize=True'") def zero_one(y_true, y_pred, normalize=False): return zero_one_loss(y_true, y_pred, normalize)
If an attribute is to be deprecated,
use the decorator
deprecated on a property.
E.g., renaming an attribute
classes_ can be done as:
@property @deprecated("Attribute labels_ was deprecated in version 0.13 and " "will be removed in 0.15. Use 'classes_' instead") def labels_(self): return self.classes_
If a parameter has to be deprecated, use
In the following example, k is deprecated and renamed to n_clusters:
import warnings def example_function(n_clusters=8, k=None): if k is not None: warnings.warn("'k' was renamed to n_clusters in version 0.13 and " "will be removed in 0.15.", DeprecationWarning) n_clusters = k
As in these examples, the warning message should always give both the version in which the deprecation happened and the version in which the old behavior will be removed. If the deprecation happened in version 0.x-dev, the message should say deprecation occurred in version 0.x and the removal will be in 0.(x+2). For example, if the deprecation happened in version 0.18-dev, the message should say it happened in version 0.18 and the old behavior will be removed in version 0.20.
In addition, a deprecation note should be added in the docstring, recalling the
same information as the deprecation warning as explained above. Use the
.. deprecated:: directive:
.. deprecated:: 0.13 ``k`` was renamed to ``n_clusters`` in version 0.13 and will be removed in 0.15.
On the top of all the functionality provided by scikit-learn. imbalanced-learn
deprecate_parameter: which is used to deprecate a sampler’s
parameter (attribute) by another one.
9.3. Testing utilities¶
Currently, imbalanced-learn provide a warning management utility. This feature is going to be merge in pytest and will be removed when the pytest release will have it.
If using Python 2.7 or above, you may use this function as a context manager:
>>> import warnings >>> from imblearn.utils.testing import warns >>> with warns(RuntimeWarning): ... warnings.warn("my runtime warning", RuntimeWarning) >>> with warns(RuntimeWarning): ... pass Traceback (most recent call last): ... Failed: DID NOT WARN. No warnings of type ...RuntimeWarning... was emitted... >>> with warns(RuntimeWarning): ... warnings.warn(UserWarning) Traceback (most recent call last): ... Failed: DID NOT WARN. No warnings of type ...RuntimeWarning... was emitted...
In the context manager form you may use the keyword argument
match to assert
that the exception matches a text or regex:
>>> import warnings >>> from imblearn.utils.testing import warns >>> with warns(UserWarning, match='must be 0 or None'): ... warnings.warn("value must be 0 or None", UserWarning) >>> with warns(UserWarning, match=r'must be \d+$'): ... warnings.warn("value must be 42", UserWarning) >>> with warns(UserWarning, match=r'must be \d+$'): ... warnings.warn("this is not here", UserWarning) Traceback (most recent call last): ... AssertionError: 'must be \d+$' pattern not found in ['this is not here']